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Equip your team with the global skills needed based on SFIA to support company's growthand digital transformation through RevoU’s tailored corporate training programs.

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2024 BRI Digital IT and Capability Building

Our curriculum is tailored to meet global digital skill demands, anchored in the

competencies framework.

TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Deep Learning 1

Overview of Deep Learning

  • Definition and key concepts
  • Historical development and milestones

Neural Networks Basics

  • Structure and components of a neural network
  • Activation functions and Forward Propagation
  • Forward and backward propagation

Introduction to Deep Learning 2

Activation Functions and Forward Propagation

  • Types and their impact on model performance
  • Commonly used activation functions in deep learning
  • Choosing the right activation function for different scenarios

Backward Propagation and Model Training

  • Gradients and optimization algorithms
  • Model Training: Hyperparameter tuning and regularization techniques
  • Hands-on Exercise: Building a simple neural network in Pytorch

Deep Learning in Finance 1

Applications of Deep Learning in Banking

  • Overview and use cases
  • Importance of deep learning in transforming banking operations
  • Future trends in AI and banking

Credit Scoring and Risk Assessment

  • Building credit scoring models with neural networks
  • Evaluating model performance in credit risk assessment
  • Practical considerations and challenges

Deep Learning in Finance 2

Fraud Detection and Prevention

  • The Autoencoder Architecture
  • Deep Learning for Anomaly Detection
  • Evaluating model performance in credit risk assessment

Customer Service Automation

  • Enhancing customer experience with AI
  • Chatbots and virtual assistants in banking
  • Real-time fraud detection using neural networks
  • Implementing automated customer support systems

Advanced Deep Learning Techniques 1: Convolution Neural Network (CNN)

Convolutional Neural Networks (CNNs)

  • Introduction and architecture of CNNs
  • Convolutional layers and feature extraction
  • Applications of CNNs in image processing

Image Recognition in Banking

  • Use cases and case studies
  • Implementing image recognition systems in financial institutions
  • Challenges and solutions in image-based deep learning applications

Computer Vision using Convolution Neural Network (CNN)

Basic of Computer Vision

  • Understanding Image Data Structures
  • Image Operation using OpenCV

Image Recognition Application

  • Image Classification
  • Data Annotation
  • Object Detection
  • Study Case: Image Recognition Application

Advanced Deep Learning Techniques 2: Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs)

  • Understanding sequential data
  • Long Short-Term Memory (LSTM) networks
  • Time Series Analysis in Finance: Predictive modeling with RNNs

Transfer Learning and Fine-tuning

  • Leveraging pre-trained models
  • Customization for Banking Applications: Adapting models for specific case
  • Hands-on Workshop: Implementing advanced techniques in Pytorch

Natural Language using Recurrent Neural Networks (RNN)

Basics of Natural Language Processing

  • Understanding Text Processing
  • Fundamental Text Processing (Bag of Word & TF-IDF)

Deep Learning for Natural Languange Processing

  • Word Tokenization
  • Understanding Embedding Layer
  • Apply RNN & LSTM to text data
  • Study Case: Text Classification

Deep Learning Implementation Strategies 1

Deep Learning Implementation

  • Real-world applications of advanced deep learning
  • Q&A session on advanced deep learning technique

Deployment Strategies

  • Cloud-based vs. on-premises deployment
  • Choosing the right deployment model for banking applications
  • Continous Improvement and Continous Delivery (CI & CD)
  • Git Workflow
  • Containerization using Docker

Deep Learning Implementation Strategies 2

Model Monitoring and Maintenance

  • Importance and challenges
  • Continuous monitoring for performance: Tools and techniques
  • Handling Concept Drift: Strategies for adapting to changing data

Model Retraining Strategies

  • Keeping models up-to-date
  • Hands-on Exercise: Deploying a model in a cloud environment
  • Case Studies: Success stories and lessons learned in model deployment

Ethical Considerations and Bias in Deep Learning 1

Ethical Guidelines in AI

  • Overview and importance of Responsible AI Practices
  • Implementing ethical principles in deep learning
  • Case Studies: Ethical considerations in banking applications

Mitigating Bias in Deep Learning Models

  • Identifying and addressing biases
  • Strategies for Bias Reduction: Techniques and best practices
  • Hands-on: Evaluating model fairness and mitigating bias

Ethical Considerations and Bias in Deep Learning 2

Regulatory Compliance and Data Privacy

  • GDPR and other regulations
  • Data Anonymization Techniques: Protecting customer privacy
  • Privacy-preserving techniques in AI applications

Federated Learning: Collaborative Model Training

  • Introduction to federated learning
  • Advantages and challenges of federated learning in banking
  • Implementing federated learning for privacy-preserving

From Transformers to Large Language Models

Transformer Models in Deep Learning

  • Introduction to Transformer architecture
  • Understanding self-attention mechanisms
  • Applications of Transformers in banking and finance

Hands-on Workshop: Implementing Transformer Models

  • Setting up and working with Transformer architectures
  • Practical exercises with attention mechanisms
  • Implementing a Transformer model for a banking use case

Generative AI Application

Generative AI: Understanding and Implementing

  • Introduction to generative AI
  • Generative AI Applications in the banking industry

LLM Generative AI (ChatGPT, Bard, etc)

  • Understanding LLM Gen AI Capability
  • Introduction to Prompt Engineering
  • LLM Study Case: Text Summarization
  • LLM Study Case: AI Chatbot

Final Project Part 1

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope

Final Project Part 2

  • End to end Deep Learning Study Case
  • Group Presentation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Machine Learning

Overview of Machine Learning

  • What is Machine Learning and what is not machine learning
  • Historical Development of AI & Machine Learning
  • Types of Machine Learning

Overview of Machine Learning application in Banking

  • Key Concept of Machine Learning Application
  • Workflow of Machine Learning
  • Example of Machine Learning Implementation in Banking

Tools in Machine Learning

  • Programming Concept in Machine Learning
  • Introduction to Programming Tools
  • Basic Python (Variable, Conditions, Loop, Function)
  • Python Library

Data Preprocessing in Machine Learning

Exploratory Data Analysis (EDA)

  • Importance of EDA in Machine Learning
  • Descriptive Statistics
  • Data Visualization Techniques
  • Identifying Patterns and Anomalies

Data Preprocessing Techniques

  • Handling Missing Data
  • Feature Scaling and Normalization
  • Encoding Categorical Variables
  • Dealing with Outliers

Practical Session

  • Applying EDA and Data Preprocessing Techniques on a Banking Dataset
  • Understanding and Implementing Machine Learning Metrics

Supervised Machine Learning

  • Understanding Key Concept of Supervised Machine Learning
  • Advantages and Disadvantages of Supervised Learning
  • Selecting Appropriate Machine Learning Type for Business Objectives

Supervised Learning (Regression & Classification) Model

Supervised Machine Learning (Regression vs Classification)

  • Distinctions between Regression and Classification
  • Choosing the Right Approach for the Task
  • Real-world Examples in Banking

Regression Model

  • Understanding Regression Model
  • Basics of Linear Regression for Regression Task
  • Assumptions and Model Interpretation
  • Hands-on Coding Exercise with a Banking Dataset

Regression Model

  • Understanding Classification Model
  • Basics of Logistic Regression for Classification Task
  • Assumptions and Model Interpretation
  • Hands-on Coding Exercise with a Banking Dataset

Advanced Supervised Learning Model

Advanced Supervised Machine Learning Model

  • Introduction to Support Vector Machines (SVM)
  • Overview of Decision Trees and Random Forest
  • Practical Implementation and Hyperparameter Tuning

Machine Learning Metrics

  • Evaluation Metrics for Regression (e.g., Mean Squared Error, R-squared)
  • Evaluation Metrics for Classification (e.g., Accuracy, Precision, Recall)
  • Selecting Appropriate Metrics for Business Objectives
  • Interpretation of Metrics Results

Model Optimization Strategies and Overfitting/Underfitting

  • Balancing Bias and Variance
  • Recognizing Overfitting and Underfitting
  • Techniques to Mitigate Overfitting (Regularization)
  • Cross-Validation Techniques
  • Hyperparameter Optimization Best Practices

Model Deployment & Guided Study Case (Capstone Project)

Machine Learning Model Deployment

  • The importance of model deployment
  • Machine Learning Deployment using Streamlit
  • Machine Learning Deployment using FastAPI

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope
  • End-to-end Project Study Case of Machine Learning in Banking
  • Group Presentation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Unsupervised Learning and Clustering

Foundations of Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Applications in the banking industry
  • Types of Unsupervised Machine Learning

Overview of Clustering in Machine Learning

  • Clustering Techniques
  • Clustering Approach
  • Example of Clustering Implementation in Banking

Clustering Algorithm

  • Clustering TechniquesK-Means Clustering
  • Hierarchical Clustering
  • Density-based clustering
  • Applications in customer segmentation

Dimensionality Reduction and Anomaly Detection

Dimensionality Reduction

  • Overview of Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • Application of Dimensionality Reduction

Anomaly Detection

  • Overview of Anomaly Detection
  • Anomaly Detection Techniques
  • Isolation Forest
  • One-Class SVM

Practical Session

  • Real-world Application in Banking
  • Case studies and use-cases in fraud detection

System Recommendation and Capstone Project

System Recommendation

  • Overview of System Recommendation
  • Content-based recommendation
  • Collaborative Filtering

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope
  • End-to-end Project Study Case of Machine Learning in Banking
  • Group Presentation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Deep Learning 1

Overview of Deep Learning

  • Definition and key concepts
  • Historical development and milestones

Neural Networks Basics

  • Structure and components of a neural network
  • Activation functions and Forward Propagation
  • Forward and backward propagation

Introduction to Deep Learning 2

Activation Functions and Forward Propagation

  • Types and their impact on model performance
  • Commonly used activation functions in deep learning
  • Choosing the right activation function for different scenarios

Backward Propagation and Model Training

  • Gradients and optimization algorithms
  • Model Training: Hyperparameter tuning and regularization techniques
  • Hands-on Exercise: Building a simple neural network in Pytorch

Deep Learning in Finance 1

Applications of Deep Learning in Banking

  • Overview and use cases
  • Importance of deep learning in transforming banking operations
  • Future trends in AI and banking

Credit Scoring and Risk Assessment

  • Building credit scoring models with neural networks
  • Evaluating model performance in credit risk assessment
  • Practical considerations and challenges

Deep Learning in Finance 2

Fraud Detection and Prevention

  • The Autoencoder Architecture
  • Deep Learning for Anomaly Detection
  • Evaluating model performance in credit risk assessment

Customer Service Automation

  • Enhancing customer experience with AI
  • Chatbots and virtual assistants in banking
  • Real-time fraud detection using neural networks
  • Implementing automated customer support systems

Advanced Deep Learning Techniques 1: Convolution Neural Network (CNN)

Convolutional Neural Networks (CNNs)

  • Introduction and architecture of CNNs
  • Convolutional layers and feature extraction
  • Applications of CNNs in image processing

Image Recognition in Banking

  • Use cases and case studies
  • Implementing image recognition systems in financial institutions
  • Challenges and solutions in image-based deep learning applications

Computer Vision using Convolution Neural Network (CNN)

Basic of Computer Vision

  • Understanding Image Data Structures
  • Image Operation using OpenCV

Image Recognition Application

  • Image Classification
  • Data Annotation
  • Object Detection
  • Study Case: Image Recognition Application

Advanced Deep Learning Techniques 2: Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs)

  • Understanding sequential data
  • Long Short-Term Memory (LSTM) networks
  • Time Series Analysis in Finance: Predictive modeling with RNNs

Transfer Learning and Fine-tuning

  • Leveraging pre-trained models
  • Customization for Banking Applications: Adapting models for specific case
  • Hands-on Workshop: Implementing advanced techniques in Pytorch

Natural Language using Recurrent Neural Networks (RNN)

Basics of Natural Language Processing

  • Understanding Text Processing
  • Fundamental Text Processing (Bag of Word & TF-IDF)

Deep Learning for Natural Languange Processing

  • Word Tokenization
  • Understanding Embedding Layer
  • Apply RNN & LSTM to text data
  • Study Case: Text Classification

Deep Learning Implementation Strategies 1

Deep Learning Implementation

  • Real-world applications of advanced deep learning
  • Q&A session on advanced deep learning technique

Deployment Strategies

  • Cloud-based vs. on-premises deployment
  • Choosing the right deployment model for banking applications
  • Continous Improvement and Continous Delivery (CI & CD)
  • Git Workflow
  • Containerization using Docker

Deep Learning Implementation Strategies 2

Model Monitoring and Maintenance

  • Importance and challenges
  • Continuous monitoring for performance: Tools and techniques
  • Handling Concept Drift: Strategies for adapting to changing data

Model Retraining Strategies

  • Keeping models up-to-date
  • Hands-on Exercise: Deploying a model in a cloud environment
  • Case Studies: Success stories and lessons learned in model deployment

Ethical Considerations and Bias in Deep Learning 1

Ethical Guidelines in AI

  • Overview and importance of Responsible AI Practices
  • Implementing ethical principles in deep learning
  • Case Studies: Ethical considerations in banking applications

Mitigating Bias in Deep Learning Models

  • Identifying and addressing biases
  • Strategies for Bias Reduction: Techniques and best practices
  • Hands-on: Evaluating model fairness and mitigating bias

Ethical Considerations and Bias in Deep Learning 2

Regulatory Compliance and Data Privacy

  • GDPR and other regulations
  • Data Anonymization Techniques: Protecting customer privacy
  • Privacy-preserving techniques in AI applications

Federated Learning: Collaborative Model Training

  • Introduction to federated learning
  • Advantages and challenges of federated learning in banking
  • Implementing federated learning for privacy-preserving

From Transformers to Large Language Models

Transformer Models in Deep Learning

  • Introduction to Transformer architecture
  • Understanding self-attention mechanisms
  • Applications of Transformers in banking and finance

Hands-on Workshop: Implementing Transformer Models

  • Setting up and working with Transformer architectures
  • Practical exercises with attention mechanisms
  • Implementing a Transformer model for a banking use case

Generative AI Application

Generative AI: Understanding and Implementing

  • Introduction to generative AI
  • Generative AI Applications in the banking industry

LLM Generative AI (ChatGPT, Bard, etc)

  • Understanding LLM Gen AI Capability
  • Introduction to Prompt Engineering
  • LLM Study Case: Text Summarization
  • LLM Study Case: AI Chatbot

Final Project Part 1

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope

Final Project Part 2

  • End to end Deep Learning Study Case
  • Group Presentation
TOTAL INVESTMENT
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TOPIC

Introduction to Machine Learning

Overview of Machine Learning

  • What is Machine Learning and what is not machine learning
  • Historical Development of AI & Machine Learning
  • Types of Machine Learning

Overview of Machine Learning application in Banking

  • Key Concept of Machine Learning Application
  • Workflow of Machine Learning
  • Example of Machine Learning Implementation in Banking

Tools in Machine Learning

  • Programming Concept in Machine Learning
  • Introduction to Programming Tools
  • Basic Python (Variable, Conditions, Loop, Function)
  • Python Library

Data Preprocessing in Machine Learning

Exploratory Data Analysis (EDA)

  • Importance of EDA in Machine Learning
  • Descriptive Statistics
  • Data Visualization Techniques
  • Identifying Patterns and Anomalies

Data Preprocessing Techniques

  • Handling Missing Data
  • Feature Scaling and Normalization
  • Encoding Categorical Variables
  • Dealing with Outliers

Practical Session

  • Applying EDA and Data Preprocessing Techniques on a Banking Dataset
  • Understanding and Implementing Machine Learning Metrics

Supervised Machine Learning

  • Understanding Key Concept of Supervised Machine Learning
  • Advantages and Disadvantages of Supervised Learning
  • Selecting Appropriate Machine Learning Type for Business Objectives

Supervised Learning (Regression & Classification) Model

Supervised Machine Learning (Regression vs Classification)

  • Distinctions between Regression and Classification
  • Choosing the Right Approach for the Task
  • Real-world Examples in Banking

Regression Model

  • Understanding Regression Model
  • Basics of Linear Regression for Regression Task
  • Assumptions and Model Interpretation
  • Hands-on Coding Exercise with a Banking Dataset

Regression Model

  • Understanding Classification Model
  • Basics of Logistic Regression for Classification Task
  • Assumptions and Model Interpretation
  • Hands-on Coding Exercise with a Banking Dataset

Advanced Supervised Learning Model

Advanced Supervised Machine Learning Model

  • Introduction to Support Vector Machines (SVM)
  • Overview of Decision Trees and Random Forest
  • Practical Implementation and Hyperparameter Tuning

Machine Learning Metrics

  • Evaluation Metrics for Regression (e.g., Mean Squared Error, R-squared)
  • Evaluation Metrics for Classification (e.g., Accuracy, Precision, Recall)
  • Selecting Appropriate Metrics for Business Objectives
  • Interpretation of Metrics Results

Model Optimization Strategies and Overfitting/Underfitting

  • Balancing Bias and Variance
  • Recognizing Overfitting and Underfitting
  • Techniques to Mitigate Overfitting (Regularization)
  • Cross-Validation Techniques
  • Hyperparameter Optimization Best Practices

Model Deployment & Guided Study Case (Capstone Project)

Machine Learning Model Deployment

  • The importance of model deployment
  • Machine Learning Deployment using Streamlit
  • Machine Learning Deployment using FastAPI

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope
  • End-to-end Project Study Case of Machine Learning in Banking
  • Group Presentation
TOTAL INVESTMENT
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Total Price* per Training Title
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TOPIC

Introduction to Unsupervised Learning and Clustering

Foundations of Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Applications in the banking industry
  • Types of Unsupervised Machine Learning

Overview of Clustering in Machine Learning

  • Clustering Techniques
  • Clustering Approach
  • Example of Clustering Implementation in Banking

Clustering Algorithm

  • Clustering TechniquesK-Means Clustering
  • Hierarchical Clustering
  • Density-based clustering
  • Applications in customer segmentation

Dimensionality Reduction and Anomaly Detection

Dimensionality Reduction

  • Overview of Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • Application of Dimensionality Reduction

Anomaly Detection

  • Overview of Anomaly Detection
  • Anomaly Detection Techniques
  • Isolation Forest
  • One-Class SVM

Practical Session

  • Real-world Application in Banking
  • Case studies and use-cases in fraud detection

System Recommendation and Capstone Project

System Recommendation

  • Overview of System Recommendation
  • Content-based recommendation
  • Collaborative Filtering

Capstone Project

  • Introduction to the Guided Case Study
  • Definition of Project Objectives and Scope
  • End-to-end Project Study Case of Machine Learning in Banking
  • Group Presentation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Defining Security Standards and Objectives

  • Aligning security standards and objectives with business goals
  • Conducting risk assessments and identifying critical assets
  • Developing security policies and procedures
  • Selecting and implementing appropriate security controls

Measuring Security Effectiveness

  • Key performance indicators (KPIs) for cybersecurity
  • Data collection and analysis for security metrics
  • Reporting and communicating security performance to stakeholders
  • Using metrics to drive continuous improvement

Case Studies and Best Practices

  • Real-world examples of successful security programs
  • Emerging trends and best practices in cybersecurity
  • Group discussion and sharing of experiences
  • Action planning for implementing security standards and objectives
TOTAL INVESTMENT
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TOPIC

Network Security Fundamentals

  • Importance of network security
  • Security threats and vulnerabilities
  • Network security architecture and concepts

Network:  Port and Vulnerability Scanning

  • Understanding network functions and configurations
  • Port scanning techniques and tools
  • Identifying common networking vulnerabilities
  • Hands-on practice with port scanning tools

Firewalls

  • Types of firewalls and their functionalities
  • Firewall configuration basics
  • Best practices for firewall deployment

Network Attacks, Architecture and Isolation

  • Common network attack types and methodologies
  • Network architecture considerations for security
  • Implementing network segmentation and isolation

Wireless and Wi-Fi Security

  • Wi-Fi vulnerabilities and common attacks
  • Secure Wi-Fi configuration practices
  • Encryption protocols and WPA standards

Network Monitoring for Threats

  • Introduction to network monitoring tools and techniques
  • Identifying suspicious activity and potential threats
  • Log analysis and incident response basics

How We Are Tracked Online

  • Online tracking methods and technologies
  • Third-party data collection and sharing practices
  • Building awareness of online privacy risks

Search Engines and Privacy

  • Search engine tracking practices and privacy settings
  • Alternative search engines with privacy focus
  • Techniques for minimizing search engine tracking

Browser Security and Tracking Prevention

  • Browser security features and settings
  • Browser extensions for privacy protection
  • Blocking tracking scripts and cookies
  • Hands-on practice with browser security tools

Case Studies and Real-World Examples

  • Discussing recent network security incidents and breaches
  • Analyzing real-world case studies and attack scenarios
  • Applying learnings to practical situations

Workshop Wrap-up and Q&A

  • Review of key workshop concepts and takeaways
  • Hands-on assessment on network security
  • Course evaluation and feedback
  • Open discussion and answering participant questions
TOTAL INVESTMENT
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TOPIC

Introduction to Endpoint Security

  • Define endpoints and explain their diverse types
  • Identify key endpoint security threats and associated risks
  • Grasp best practices and solutions for effective endpoint protection

Anti-Virus and Endpoint Protection

  • Differentiate traditional anti-virus from Next-Generation (NGAV) endpoint protection technologies
  • Understand the capabilities and limitations of different NGAV solutions
  • Select and implement an appropriate endpoint protection solution based on organizational needs

Threat Detection and Monitoring

  • Master various techniques and tools for detecting and monitoring threats on endpoints
  • Conduct effective security incident investigations and implement appropriate responses
  • Apply active hunting methods to discover malware and attackers within your network

Operating System and Application Hardening

  • Apply effective hardening techniques for popular operating systems like Windows, macOS, and Linux
  • Implement robust application and software security measures
  • Understand and employ the least privilege principle for secure access configuration

Secure Deleting, Evidence Elimination, and Anti-Forensics

  • Implement secure deleting techniques to permanently erase sensitive data
  • Understand methods for eliminating digital evidence and preventing digital forensics investigations
  • Learn strategies to hinder the recovery of deleted data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Operational Security

  • Introducing the concept of operational security
  • Best practices for maintaining online confidentiality and anonymity
  • Preventing and mitigating tracking and surveillance techniques

Live Operating Systems

  • Introduction and comparison of secure live operating systems
  • Selecting the right OS based on individual needs
  • Installation and configuration of live operating systems

VPNs and Tor Browser

  • Understanding how VPNs and Tor Browser protect online privacy
  • Selecting and using a secure VPN
  • Configuring and using Tor Browser for anonymity

Proxies

  • Types of proxies (HTTP, HTTPS, SOCK, and Web) and their functions
  • Selecting and using appropriate proxies
  • Configuring proxies across various applications

Secure Shell (SSH)

  • Introducing SSH and its benefits for secure communication
  • Establishing and managing secure SSH connections
  • Using SSH for file transfer and remote access

The Invisible Internet Project (12P)

  • Introducing 12P and its services for online anonymity
  • Using 12P for secure communication

Review and Q&A

  • Key takeaways from the training materials
  • Discussion and question-answer session
  • Training evaluation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

The Cybersecurity Landscape

  • Understand the core concepts of cybersecurity (CIA triad, confidentiality, integrity, availability)
  • Identify common threats and vulnerabilities

Information Security Vectors, Hacking, and Controls

  • Recognize common attack vectors (phishing, malware, social engineering)
  • Analyze attack stages, and implement basic cybersecurity controls (authentication, authorization, encryption, firewalls, intrusion detection/prevention systems)

Installation of Virtual Operating System

  • Set up a virtual environment (VirtualBox or VMware)
  • Install a secure operating system (e.g., Kali Linux) for hands-on practice

Network and Web Application Scanning

  • Utilize network scanning tools (Nmap, Nessus) to identify vulnerabilities in networks and web applications
  • Interpret scan results
  • Prioritize remediation efforts.

Password Cracking Technique

  • Comprehend common password vulnerabilities
  • Explore password cracking techniques (brute force, dictionary attacks, rainbow tables)
  • Apply password best practices for mitigation

Network Pentesting using Metasploit

  • Introduce the Metasploit Framework
  • Exploit vulnerabilities using pre-configured modules
  • Gain access to systems
  • Perform post-exploitation activities in a safe, controlled environment
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Defining Security Standards and Objectives

  • Aligning security standards and objectives with business goals
  • Conducting risk assessments and identifying critical assets
  • Developing security policies and procedures
  • Selecting and implementing appropriate security controls

Measuring Security Effectiveness

  • Key performance indicators (KPIs) for cybersecurity
  • Data collection and analysis for security metrics
  • Reporting and communicating security performance to stakeholders
  • Using metrics to drive continuous improvement

Case Studies and Best Practices

  • Real-world examples of successful security programs
  • Emerging trends and best practices in cybersecurity
  • Group discussion and sharing of experiences
  • Action planning for implementing security standards and objectives
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Network Security Fundamentals

  • Importance of network security
  • Security threats and vulnerabilities
  • Network security architecture and concepts

Network:  Port and Vulnerability Scanning

  • Understanding network functions and configurations
  • Port scanning techniques and tools
  • Identifying common networking vulnerabilities
  • Hands-on practice with port scanning tools

Firewalls

  • Types of firewalls and their functionalities
  • Firewall configuration basics
  • Best practices for firewall deployment

Network Attacks, Architecture and Isolation

  • Common network attack types and methodologies
  • Network architecture considerations for security
  • Implementing network segmentation and isolation

Wireless and Wi-Fi Security

  • Wi-Fi vulnerabilities and common attacks
  • Secure Wi-Fi configuration practices
  • Encryption protocols and WPA standards

Network Monitoring for Threats

  • Introduction to network monitoring tools and techniques
  • Identifying suspicious activity and potential threats
  • Log analysis and incident response basics

How We Are Tracked Online

  • Online tracking methods and technologies
  • Third-party data collection and sharing practices
  • Building awareness of online privacy risks

Search Engines and Privacy

  • Search engine tracking practices and privacy settings
  • Alternative search engines with privacy focus
  • Techniques for minimizing search engine tracking

Browser Security and Tracking Prevention

  • Browser security features and settings
  • Browser extensions for privacy protection
  • Blocking tracking scripts and cookies
  • Hands-on practice with browser security tools

Case Studies and Real-World Examples

  • Discussing recent network security incidents and breaches
  • Analyzing real-world case studies and attack scenarios
  • Applying learnings to practical situations

Workshop Wrap-up and Q&A

  • Review of key workshop concepts and takeaways
  • Hands-on assessment on network security
  • Course evaluation and feedback
  • Open discussion and answering participant questions
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Endpoint Security

  • Define endpoints and explain their diverse types
  • Identify key endpoint security threats and associated risks
  • Grasp best practices and solutions for effective endpoint protection

Anti-Virus and Endpoint Protection

  • Differentiate traditional anti-virus from Next-Generation (NGAV) endpoint protection technologies
  • Understand the capabilities and limitations of different NGAV solutions
  • Select and implement an appropriate endpoint protection solution based on organizational needs

Threat Detection and Monitoring

  • Master various techniques and tools for detecting and monitoring threats on endpoints
  • Conduct effective security incident investigations and implement appropriate responses
  • Apply active hunting methods to discover malware and attackers within your network

Operating System and Application Hardening

  • Apply effective hardening techniques for popular operating systems like Windows, macOS, and Linux
  • Implement robust application and software security measures
  • Understand and employ the least privilege principle for secure access configuration

Secure Deleting, Evidence Elimination, and Anti-Forensics

  • Implement secure deleting techniques to permanently erase sensitive data
  • Understand methods for eliminating digital evidence and preventing digital forensics investigations
  • Learn strategies to hinder the recovery of deleted data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Operational Security

  • Introducing the concept of operational security
  • Best practices for maintaining online confidentiality and anonymity
  • Preventing and mitigating tracking and surveillance techniques

Live Operating Systems

  • Introduction and comparison of secure live operating systems
  • Selecting the right OS based on individual needs
  • Installation and configuration of live operating systems

VPNs and Tor Browser

  • Understanding how VPNs and Tor Browser protect online privacy
  • Selecting and using a secure VPN
  • Configuring and using Tor Browser for anonymity

Proxies

  • Types of proxies (HTTP, HTTPS, SOCK, and Web) and their functions
  • Selecting and using appropriate proxies
  • Configuring proxies across various applications

Secure Shell (SSH)

  • Introducing SSH and its benefits for secure communication
  • Establishing and managing secure SSH connections
  • Using SSH for file transfer and remote access

The Invisible Internet Project (12P)

  • Introducing 12P and its services for online anonymity
  • Using 12P for secure communication

Review and Q&A

  • Key takeaways from the training materials
  • Discussion and question-answer session
  • Training evaluation
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

The Cybersecurity Landscape

  • Understand the core concepts of cybersecurity (CIA triad, confidentiality, integrity, availability)
  • Identify common threats and vulnerabilities

Information Security Vectors, Hacking, and Controls

  • Recognize common attack vectors (phishing, malware, social engineering)
  • Analyze attack stages, and implement basic cybersecurity controls (authentication, authorization, encryption, firewalls, intrusion detection/prevention systems)

Installation of Virtual Operating System

  • Set up a virtual environment (VirtualBox or VMware)
  • Install a secure operating system (e.g., Kali Linux) for hands-on practice

Network and Web Application Scanning

  • Utilize network scanning tools (Nmap, Nessus) to identify vulnerabilities in networks and web applications
  • Interpret scan results
  • Prioritize remediation efforts.

Password Cracking Technique

  • Comprehend common password vulnerabilities
  • Explore password cracking techniques (brute force, dictionary attacks, rainbow tables)
  • Apply password best practices for mitigation

Network Pentesting using Metasploit

  • Introduce the Metasploit Framework
  • Exploit vulnerabilities using pre-configured modules
  • Gain access to systems
  • Perform post-exploitation activities in a safe, controlled environment
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cyber Threat Intelligence (CTI)

  • Understanding the CTI landscape and its role in cybersecurity
  • Key concepts: threat actors, indicators of compromise (IOCs), intelligence cycle
  • Benefits and limitations of CTI
  • CTI applications in different organizational contexts
  • Interactive workshop: Identifying threats and vulnerabilities in a case study

Cybersecurity Fundamentals

  • Essential security concepts: confidentiality, integrity, availability (CIA triad)
  • Common attack vectors and mitigation strategies
  • Network security principles and controls
  • Hands-on activity: Simulating network attacks and defenses using a virtual environment

Open-Source Intelligence (OSINT) for CTI

  • Techniques for gathering data from publicly available sources
  • Tools and platforms for effective OSINT collection
  • Ethical considerations and legal boundaries
  • Group exercise: Conducting an OSINT investigation on a specific threat actor

Advanced Threat Analysis and Reporting

  • Structured analytical techniques (SATs) for CTI analysis
  • Threat reporting best practices: clarity, conciseness, actionable insights
  • Communication strategies for different audiences (technical, managerial)
  • Collaborative analysis and threat sharing across teams
  • Case study: Analyzing a complex cyberattack and generating a comprehensive report

Threat Actor Profiling and Attribution

  • Common threat actor groups and their motivations
  • Tactics, techniques, and procedures (TTPs) analysis
  • Attribution challenges and methodologies
  • Group activity: Profiling a real-world threat actor based on intelligence reports

CTI Integration with Security Tools and Processes

  • SIEM/SOC integration for threat detection and response
  • SOAR automation to streamline CTI workflows
  • Continuous threat monitoring and incident response using CTI insights
  • Demonstration: Integrating CTI feeds into a SIEM platform

Ethical and Legal Considerations in CTI

  • SIEM/SOC integration for threat detection and response
  • SOAR automation to streamline CTI workflows
  • Continuous threat monitoring and incident response using CTI insights
  • Demonstration: Integrating CTI feeds into a SIEM platform

The Future of CTI: Emerging Trends and Technologies

  • Artificial intelligence (AI) and machine learning (ML) applications in CTI
  • Cyber threat sharing platforms and communities
  • The future of threat actor behavior and attack evolution
  • Group discussion: Brainstorming future developments and implications for CTI

Capstone Project (Optional)

  • Apply learned skills to a simulated real-world scenario
  • Conduct a comprehensive threat intelligence investigation
  • Develop a mitigation plan based on analysis
  • Present findings and recommendations
TOTAL INVESTMENT
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Total Price* per Training Title
TOPIC

Phishing Awareness

  • Definition and types of phishing attacks (email, phone, SMS, etc.)
  • Red flags to identify phishing attempts (suspicious sender, urgent tone, grammar errors, etc.)
  • Best practices for safe email handling and password management
  • Interactive quiz and phishing simulation exercise

Data Breach Awareness

  • Common causes of data breaches (accidental leaks, malicious attacks, etc.)
  • Impact of data breaches on individuals and organizations (financial loss, reputational damage, legal consequences)
  • Importance of data protection and compliance regulations
  • Case studies of real-world data breaches

Dealing with Ransomware

  • Definition and how ransomware works (encryption, data exfiltration)
  • Different types of ransomware attacks and their targets
  • Consequences of ransomware infection (operational disruption, data loss, ransom demands)
  • Best practices for preventing ransomware attacks (software updates, backups, awareness training)

Introduction to Incident Response Management (IRM)

  • Definition and importance of IRM in cybersecurity
  • Key phases of the IRM framework (preparation, identification, containment, eradication, recovery, post-incident activities)
  • Overview of roles and responsibilities within an IRM team

Deep Dive into IRM Phases

  • Preparation: Develop an IR plan, conduct risk assessments, establish communication protocols
  • Identification: Detect security incidents through monitoring and reporting procedures
  • Containment: Minimize the impact of the incident by isolating affected systems and stopping the attack
  • Eradication: Remove the threat and identify the root cause of the incident
  • Recovery: Restore compromised systems and data following established procedures
  • Post-Incident Activities: Document lessons learned, improve security posture, and conduct post-incident reviews

IRM Scenario Practices

  • Divide participants into teams and simulate different cyber incidents (phishing attack, data breach, ransomware infection)
  • Teams apply IRM framework steps to each scenario, discuss decision-making, and identify challenges
  • Debriefing and discussion of team responses and lessons learned

Communication Strategies

  • Importance of clear and timely communication during an incident
  • Internal communication protocols for informing leadership, IT teams, and affected users
  • External communication strategies for communicating with customers, partners, and regulators
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to IAM

  • What is IAM and why is it important?
  • Key IAM components and technologies (e.g., Active Directory, LDAP, SAML, OAuth)
  • IAM risks and challenges

AAA Framework

  • Identification: User registration and management
  • Authentication: Methods and factors (e.g., passwords, multi-factor authentication)
  • Authorization: Roles, permissions, and access control models (e.g., RBAC, ABAC)
  • Accounting: Monitoring and logging user activity

Privileged Account Management (PAM)

  • Why PAM is critical for security
  • Identifying and classifying privileged accounts
  • Least privilege principle and enforcing it
  • Secure password management for privileged accounts
  • Monitoring and auditing privileged user activity

Identity Governance and Administration (IGA)

  • Access request and provisioning processes
  • Identity lifecycle management
  • User self-service options
  • Role-based access control (RBAC) and fine-grained access control (ABAC)
  • Identity reconciliation and synchronization

Data Governance and Protection

  • Understanding data classification and sensitivity levels
  • Data access control principles and best practices
  • Securing data at rest, in transit, and in use
  • Integrating IAM with data protection solutions
  • Compliance considerations for data governance

Hands-on Exercise

  • Implementing multi-factor authentication
  • Configuring role-based access control
  • Granting and revoking user access
  • Monitoring privileged user activity
TOTAL INVESTMENT
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TOPIC

DevSecOps Introduction

  • DevSecOps principles and benefits
  • Security challenges in DevOps environment
  • Shifting security left philosophy
  • Future of DevSecOps landscape

Hands-on: Virtual Machine Setup

  • Setting up virtual machines using tools like VirtualBox or Vagrant
  • Pre-installation of required software on VMs
  • User and account management

Jenkins Installation and Configuration

  • Installing Jenkins on a dedicated VM
  • Configuring plugins and security settings
  • Creating initial Jenkins jobs

Application Deployment

  • Multiplayer snake application setup
  • Running the application on an application server
  • Mapping domain name to application server IP address

Jenkins Pipeline Creation

  • Introduction to Pipeline syntax and functionalities
  • Building a CI/CD pipeline for the snake application
  • Integration with Git repository and build tools
  • Automating deployments and testing using pipeline stages

Source Code Security

  • Static code analysis (SAST) tools and techniques
  • Identifying and mitigating vulnerabilities in code
  • Integrating SAST tools into the pipeline

Security Scanning

  • Dynamic Application Security Testing (DAST) principles
  • Utilizing DAST tools to identify runtime vulnerabilities
  • Automating DAST scans in the pipeline

Container Security

  • Introduction to containerization and Docker
  • Security considerations for container images and deployments
  • Vulnerability scanning and container registries
  • Secure container development practices

Hands-on: Containerized Application

  • Containerizing the snake application using Dockerfile
  • Building and pushing container images to a registry
  • Running containerized applications in Kubernetes

DevSecOps Tools and Automation (Optional)

  • Overview of popular DevSecOps tools and platforms
  • Automating security tasks in the CI/CD pipeline
  • Integrating security findings into development workflows

Introduction Continuous Monitoring and Logging (Optional)

  • Monitoring application security posture and infrastructure
  • Logging for security incident detection and analysis
  • Utilizing security information and event management (SIEM) tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Network Fundamentals

  • What is vulnerability assessment?
  • Why is vulnerability assessment important?
  • Benefits of conducting vulnerability assessments
  • Different types of vulnerability assessments

Introduction to Network Security

  • Defining security threats and vulnerabilities in network environments
  • Understanding common attack vectors and techniques
  • Introduction to network security controls like firewalls, intrusion detection/prevention systems (IDS/IPS), and access control lists (ACLs)

Securing Network Communications

  • Importance of encryption and its role in secure communication
  • Understanding and configuring secure protocols like HTTPS and VPN
  • Practical hands-on with tools like Wireshark to analyze network traffic and identify potential security issues

Database Security Basics

  • Introduction to different types of databases and their security considerations
  • Understanding the concepts of data integrity, confidentiality, and availability
  • Identifying common database vulnerabilities like SQL injection and cross-site scripting (XSS)

Database Access Control and Authentication

  • Implementing user authentication and authorization mechanisms for database access
  • Understanding the principles of least privilege and role-based access control (RBAC)
  • Practical demonstration of user management and access control techniques

Data Encryption and Security Practices

  • Importance of data encryption at rest and in transit for secure storage and transmission
  • Understanding and implementing encryption technologies like AES and RSA
  • Exploring best practices for data security, including backups, disaster recovery, and vulnerability management

Network Security Threats and Mitigation Strategies

  • Detailed exploration of common network attacks like denial-of-service (DoS), man-in-the-middle (MitM), and malware attacks
  • Understanding and implementing mitigation strategies for different types of network threats
  • Practical exercises using tools like nmap and Metasploit to simulate and analyze security vulnerabilities

Advanced Database Security Techniques

  • Exploring advanced security features like intrusion detection/prevention systems (IDS/IPS) for databases
  • Understanding data masking and tokenization techniques for sensitive information protection
  • Implementing security measures for database auditing and logging

Security Incident Response and Forensics

  • Understanding the importance of incident response planning and procedures
  • Learning basic forensics techniques for identifying and analyzing security incidents
  • Practical exercises simulating incident response scenarios and practicing evidence collection and analysis

(Optional) Security Tools and Resources

  • Exploring popular open-source and commercial tools for network and database security assessments and monitoring
  • Understanding the role of security frameworks and standards like NIST Cybersecurity Framework and ISO 27001
  • Discussing best practices for staying updated on the latest security threats and vulnerabilities

(Optional) Case Studies and Real-world Examples

  • Analyzing real-world case studies of successful network breaches and database security incidents
  • Learning from the mistakes of others and discussing strategies to avoid similar vulnerabilities
  • Group discussions and brainstorming solutions to address emerging security challenges
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Vulnerability Assessment

  • What is vulnerability assessment?
  • Why is vulnerability assessment important?
  • Benefits of conducting vulnerability assessments
  • Different types of vulnerability assessments

Types of Vulnerabilities

  • Common network vulnerabilities
  • Server and web application vulnerabilities
  • Remote service vulnerabilities
  • Understanding CVSS scoring system

Network Vulnerability Assessment

  • Using Nmap and Nessus for network scanning
  • Identifying and prioritizing network vulnerabilities
  • Conducting network security assessments

Server and Web Application Vulnerability Assessment

  • Using Nessus and OWASP ZAP for server and web application scanning
  • Identifying and prioritizing server and web application vulnerabilities
  • Conducting web application security assessments

Remote Service Vulnerability Assessment

  • Assessing vulnerabilities in VPN services
  • Identifying and prioritizing remote service vulnerabilities
  • Conducting remote service security assessments

Vulnerability Assessment Tools

  • Nmap
  • Nessus
  • OWASP ZAP
  • Other industry-standard tools

Vulnerability Assessment Analysis

  • Analyzing vulnerability assessment results
  • Prioritizing remediation efforts
  • Generating vulnerability reports

Best Practices for Vulnerability Assessment

  • Scheduling regular vulnerability assessments
  • Integrating vulnerability assessment into the security lifecycle
  • Maintaining a vulnerability management program
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Cyber Threat Intelligence (CTI)

  • Understanding the CTI landscape and its role in cybersecurity
  • Key concepts: threat actors, indicators of compromise (IOCs), intelligence cycle
  • Benefits and limitations of CTI
  • CTI applications in different organizational contexts
  • Interactive workshop: Identifying threats and vulnerabilities in a case study

Cybersecurity Fundamentals

  • Essential security concepts: confidentiality, integrity, availability (CIA triad)
  • Common attack vectors and mitigation strategies
  • Network security principles and controls
  • Hands-on activity: Simulating network attacks and defenses using a virtual environment

Open-Source Intelligence (OSINT) for CTI

  • Techniques for gathering data from publicly available sources
  • Tools and platforms for effective OSINT collection
  • Ethical considerations and legal boundaries
  • Group exercise: Conducting an OSINT investigation on a specific threat actor

Advanced Threat Analysis and Reporting

  • Structured analytical techniques (SATs) for CTI analysis
  • Threat reporting best practices: clarity, conciseness, actionable insights
  • Communication strategies for different audiences (technical, managerial)
  • Collaborative analysis and threat sharing across teams
  • Case study: Analyzing a complex cyberattack and generating a comprehensive report

Threat Actor Profiling and Attribution

  • Common threat actor groups and their motivations
  • Tactics, techniques, and procedures (TTPs) analysis
  • Attribution challenges and methodologies
  • Group activity: Profiling a real-world threat actor based on intelligence reports

CTI Integration with Security Tools and Processes

  • SIEM/SOC integration for threat detection and response
  • SOAR automation to streamline CTI workflows
  • Continuous threat monitoring and incident response using CTI insights
  • Demonstration: Integrating CTI feeds into a SIEM platform

Ethical and Legal Considerations in CTI

  • SIEM/SOC integration for threat detection and response
  • SOAR automation to streamline CTI workflows
  • Continuous threat monitoring and incident response using CTI insights
  • Demonstration: Integrating CTI feeds into a SIEM platform

The Future of CTI: Emerging Trends and Technologies

  • Artificial intelligence (AI) and machine learning (ML) applications in CTI
  • Cyber threat sharing platforms and communities
  • The future of threat actor behavior and attack evolution
  • Group discussion: Brainstorming future developments and implications for CTI

Capstone Project (Optional)

  • Apply learned skills to a simulated real-world scenario
  • Conduct a comprehensive threat intelligence investigation
  • Develop a mitigation plan based on analysis
  • Present findings and recommendations
TOTAL INVESTMENT
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(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Phishing Awareness

  • Definition and types of phishing attacks (email, phone, SMS, etc.)
  • Red flags to identify phishing attempts (suspicious sender, urgent tone, grammar errors, etc.)
  • Best practices for safe email handling and password management
  • Interactive quiz and phishing simulation exercise

Data Breach Awareness

  • Common causes of data breaches (accidental leaks, malicious attacks, etc.)
  • Impact of data breaches on individuals and organizations (financial loss, reputational damage, legal consequences)
  • Importance of data protection and compliance regulations
  • Case studies of real-world data breaches

Dealing with Ransomware

  • Definition and how ransomware works (encryption, data exfiltration)
  • Different types of ransomware attacks and their targets
  • Consequences of ransomware infection (operational disruption, data loss, ransom demands)
  • Best practices for preventing ransomware attacks (software updates, backups, awareness training)

Introduction to Incident Response Management (IRM)

  • Definition and importance of IRM in cybersecurity
  • Key phases of the IRM framework (preparation, identification, containment, eradication, recovery, post-incident activities)
  • Overview of roles and responsibilities within an IRM team

Deep Dive into IRM Phases

  • Preparation: Develop an IR plan, conduct risk assessments, establish communication protocols
  • Identification: Detect security incidents through monitoring and reporting procedures
  • Containment: Minimize the impact of the incident by isolating affected systems and stopping the attack
  • Eradication: Remove the threat and identify the root cause of the incident
  • Recovery: Restore compromised systems and data following established procedures
  • Post-Incident Activities: Document lessons learned, improve security posture, and conduct post-incident reviews

IRM Scenario Practices

  • Divide participants into teams and simulate different cyber incidents (phishing attack, data breach, ransomware infection)
  • Teams apply IRM framework steps to each scenario, discuss decision-making, and identify challenges
  • Debriefing and discussion of team responses and lessons learned

Communication Strategies

  • Importance of clear and timely communication during an incident
  • Internal communication protocols for informing leadership, IT teams, and affected users
  • External communication strategies for communicating with customers, partners, and regulators
TOTAL INVESTMENT
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Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to IAM

  • What is IAM and why is it important?
  • Key IAM components and technologies (e.g., Active Directory, LDAP, SAML, OAuth)
  • IAM risks and challenges

AAA Framework

  • Identification: User registration and management
  • Authentication: Methods and factors (e.g., passwords, multi-factor authentication)
  • Authorization: Roles, permissions, and access control models (e.g., RBAC, ABAC)
  • Accounting: Monitoring and logging user activity

Privileged Account Management (PAM)

  • Why PAM is critical for security
  • Identifying and classifying privileged accounts
  • Least privilege principle and enforcing it
  • Secure password management for privileged accounts
  • Monitoring and auditing privileged user activity

Identity Governance and Administration (IGA)

  • Access request and provisioning processes
  • Identity lifecycle management
  • User self-service options
  • Role-based access control (RBAC) and fine-grained access control (ABAC)
  • Identity reconciliation and synchronization

Data Governance and Protection

  • Understanding data classification and sensitivity levels
  • Data access control principles and best practices
  • Securing data at rest, in transit, and in use
  • Integrating IAM with data protection solutions
  • Compliance considerations for data governance

Hands-on Exercise

  • Implementing multi-factor authentication
  • Configuring role-based access control
  • Granting and revoking user access
  • Monitoring privileged user activity
TOTAL INVESTMENT
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Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

DevSecOps Introduction

  • DevSecOps principles and benefits
  • Security challenges in DevOps environment
  • Shifting security left philosophy
  • Future of DevSecOps landscape

Hands-on: Virtual Machine Setup

  • Setting up virtual machines using tools like VirtualBox or Vagrant
  • Pre-installation of required software on VMs
  • User and account management

Jenkins Installation and Configuration

  • Installing Jenkins on a dedicated VM
  • Configuring plugins and security settings
  • Creating initial Jenkins jobs

Application Deployment

  • Multiplayer snake application setup
  • Running the application on an application server
  • Mapping domain name to application server IP address

Jenkins Pipeline Creation

  • Introduction to Pipeline syntax and functionalities
  • Building a CI/CD pipeline for the snake application
  • Integration with Git repository and build tools
  • Automating deployments and testing using pipeline stages

Source Code Security

  • Static code analysis (SAST) tools and techniques
  • Identifying and mitigating vulnerabilities in code
  • Integrating SAST tools into the pipeline

Security Scanning

  • Dynamic Application Security Testing (DAST) principles
  • Utilizing DAST tools to identify runtime vulnerabilities
  • Automating DAST scans in the pipeline

Container Security

  • Introduction to containerization and Docker
  • Security considerations for container images and deployments
  • Vulnerability scanning and container registries
  • Secure container development practices

Hands-on: Containerized Application

  • Containerizing the snake application using Dockerfile
  • Building and pushing container images to a registry
  • Running containerized applications in Kubernetes

DevSecOps Tools and Automation (Optional)

  • Overview of popular DevSecOps tools and platforms
  • Automating security tasks in the CI/CD pipeline
  • Integrating security findings into development workflows

Introduction Continuous Monitoring and Logging (Optional)

  • Monitoring application security posture and infrastructure
  • Logging for security incident detection and analysis
  • Utilizing security information and event management (SIEM) tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Vulnerability Assessment

  • What is vulnerability assessment?
  • Why is vulnerability assessment important?
  • Benefits of conducting vulnerability assessments
  • Different types of vulnerability assessments

Types of Vulnerabilities

  • Common network vulnerabilities
  • Server and web application vulnerabilities
  • Remote service vulnerabilities
  • Understanding CVSS scoring system

Network Vulnerability Assessment

  • Using Nmap and Nessus for network scanning
  • Identifying and prioritizing network vulnerabilities
  • Conducting network security assessments

Server and Web Application Vulnerability Assessment

  • Using Nessus and OWASP ZAP for server and web application scanning
  • Identifying and prioritizing server and web application vulnerabilities
  • Conducting web application security assessments

Remote Service Vulnerability Assessment

  • Assessing vulnerabilities in VPN services
  • Identifying and prioritizing remote service vulnerabilities
  • Conducting remote service security assessments

Vulnerability Assessment Tools

  • Nmap
  • Nessus
  • OWASP ZAP
  • Other industry-standard tools

Vulnerability Assessment Analysis

  • Analyzing vulnerability assessment results
  • Prioritizing remediation efforts
  • Generating vulnerability reports

Best Practices for Vulnerability Assessment

  • Scheduling regular vulnerability assessments
  • Integrating vulnerability assessment into the security lifecycle
  • Maintaining a vulnerability management program
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Designing Efficient and Reliable Solutions

Understanding Design Patterns

  • Introduction to design patterns and their benefits
  • Common design patterns used in cloud computing

Designing for Scalability and Performance

  • Principles of designing scalable and performant cloud solutions
  • Techniques for optimizing resource utilization

Designing for Reliability and Availability

  • Strategies for ensuring high availability and reliability
  • Techniques for building fault-tolerant systems

Designing for Security and Compliance

  • Best practices for designing secure and compliant cloud solutions
  • Techniques for meeting regulatory requirements

Optimizing Performance

Monitoring and Analyzing Performance

  • Tools and techniques for monitoring cloud performance
  • Identifying and resolving performance bottlenecks

Optimizing Compute Resources

  • Techniques for optimizing the performance of compute resources
  • Strategies for rightsizing and autoscaling

Optimizing Storage Resources

  • Techniques for optimizing the performance of storage resources
  • Strategies for choosing the right storage type

Optimizing Network Resources

  • Techniques for optimizing the performance of network resources
  • Strategies for designing efficient network topologie

Implementing Security and Compliance

Securing Compute Resources

  • Techniques for securing compute resources
  • Best practices for managing user access and permissions

Securing Storage Resources

  • Techniques for securing storage resources
  • Best practices for data encryption and access control

Securing Network Resources

  • Techniques for securing network resources
  • Best practices for firewall configuration and intrusion detection

Maintaining Compliance

  • Strategies for maintaining compliance with regulatory requirements
  • Best practices for auditing and reporting

Case Studies and Best Practices

  • Review of real-world case studies demonstrating the application of design patterns and best practices
  • Discussion of best practices for designing, implementing, and managing efficient, reliable, secure, and compliant cloud solutions
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cloud Computing

  • Key concepts and benefits of cloud computing
  • Differentiating between public, private, and hybrid clouds
  • Introduction to AWS as a public cloud provider

Introduction to AWS Architecture

  • Core AWS components: regions, Availability Zones, and security groups
  • Understanding VPCs (Virtual Private Clouds) and subnets
  • AWS Identity and Access Management (IAM) basics

Introduction of AWS Compute Services

  • Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
  • Amazon Lambda: Serverless compute service for running code without managing servers
  • Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
  • Hands-on: Launching an EC2 instance and exploring its console
  • Hands-on: Creating a simple Lambda function

Introduction of AWS Storage Services

  • Amazon S3: Object storage for any type of data, scalable and durable
  • Amazon EBS: Block storage for EC2 instances, providing persistent volumes
  • Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
  • Amazon DynamoDB: NoSQL database for flexible, scalable data storage
  • Containerization with Docker and Amazon ECS

Introduction of AWS Networking Services

  • VPC (Virtual Private Cloud) creation and management
  • Subnets and network security groups
  • Internet Gateways and NAT Gateways for outbound traffic
  • Route 53 for domain name registration and DNS management

Introduction of AWS Security Services

  • IAM basics: Access policies, roles, and users
  • AWS Security Groups and network access control
  • Amazon Cognito: User authentication and authorization
  • Amazon Key Management Service (KMS): Secure storage and management of encryption keys

Introduction of AWS Monitoring

  • Amazon CloudWatch for monitoring metrics and logs
  • CloudTrail for auditing AWS API calls
  • Amazon CloudFormation for infrastructure as code (IaC)
  • Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

Deployment Application on AWS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications in AWS
  • Hands-on: Creating a simple Lambda function  and connecting it to S3

Operate and Monitor - Application Monitoring with AWS CloudWatch

  • Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
  • Visualizing performance metrics and dashboards
  • Correlating logs and events with metrics for troubleshooting

Operate and Monitor - Logging with AWS CloudWatch Logs

  • Collecting and ingesting application logs from various sources
  • Filtering and analyzing logs for root cause analysis
  • Integrating CloudWatch Logs with other services for further analysis
  • Creating and routing events based on triggers (e.g., resource state changes, alarms)
  • Integrating events with other AWS services for automated actions.
    Implementing incident management workflow with CloudWatch Events

Operate and Monitor - Hands on Labs

  • Monitor a sample application with CloudWatch metrics, logs, and events
  • Create alarms and automate actions based on monitoring data
  • Simulate and troubleshoot application issues using CloudWatch tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Designing Efficient and Reliable Solutions

Understanding Design Patterns

  • Introduction to design patterns and their benefits
  • Common design patterns used in cloud computing

Designing for Scalability and Performance

  • Principles of designing scalable and performant cloud solutions
  • Techniques for optimizing resource utilization

Designing for Reliability and Availability

  • Strategies for ensuring high availability and reliability
  • Techniques for building fault-tolerant systems

Designing for Security and Compliance

  • Best practices for designing secure and compliant cloud solutions
  • Techniques for meeting regulatory requirements

Optimizing Performance

Monitoring and Analyzing Performance

  • Tools and techniques for monitoring cloud performance
  • Identifying and resolving performance bottlenecks

Optimizing Compute Resources

  • Techniques for optimizing the performance of compute resources
  • Strategies for rightsizing and autoscaling

Optimizing Storage Resources

  • Techniques for optimizing the performance of storage resources
  • Strategies for choosing the right storage type

Optimizing Network Resources

  • Techniques for optimizing the performance of network resources
  • Strategies for designing efficient network topologie

Implementing Security and Compliance

Securing Compute Resources

  • Techniques for securing compute resources
  • Best practices for managing user access and permissions

Securing Storage Resources

  • Techniques for securing storage resources
  • Best practices for data encryption and access control

Securing Network Resources

  • Techniques for securing network resources
  • Best practices for firewall configuration and intrusion detection

Maintaining Compliance

  • Strategies for maintaining compliance with regulatory requirements
  • Best practices for auditing and reporting

Case Studies and Best Practices

  • Review of real-world case studies demonstrating the application of design patterns and best practices
  • Discussion of best practices for designing, implementing, and managing efficient, reliable, secure, and compliant cloud solutions
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Cloud Computing

  • Key concepts and benefits of cloud computing
  • Differentiating between public, private, and hybrid clouds
  • Introduction to AWS as a public cloud provider

Introduction to AWS Architecture

  • Core AWS components: regions, Availability Zones, and security groups
  • Understanding VPCs (Virtual Private Clouds) and subnets
  • AWS Identity and Access Management (IAM) basics

Introduction of AWS Compute Services

  • Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
  • Amazon Lambda: Serverless compute service for running code without managing servers
  • Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
  • Hands-on: Launching an EC2 instance and exploring its console
  • Hands-on: Creating a simple Lambda function

Introduction of AWS Storage Services

  • Amazon S3: Object storage for any type of data, scalable and durable
  • Amazon EBS: Block storage for EC2 instances, providing persistent volumes
  • Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
  • Amazon DynamoDB: NoSQL database for flexible, scalable data storage
  • Containerization with Docker and Amazon ECS

Introduction of AWS Networking Services

  • VPC (Virtual Private Cloud) creation and management
  • Subnets and network security groups
  • Internet Gateways and NAT Gateways for outbound traffic
  • Route 53 for domain name registration and DNS management

Introduction of AWS Security Services

  • IAM basics: Access policies, roles, and users
  • AWS Security Groups and network access control
  • Amazon Cognito: User authentication and authorization
  • Amazon Key Management Service (KMS): Secure storage and management of encryption keys

Introduction of AWS Monitoring

  • Amazon CloudWatch for monitoring metrics and logs
  • CloudTrail for auditing AWS API calls
  • Amazon CloudFormation for infrastructure as code (IaC)
  • Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

Deployment Application on AWS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications in AWS
  • Hands-on: Creating a simple Lambda function  and connecting it to S3

Operate and Monitor - Application Monitoring with AWS CloudWatch

  • Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
  • Visualizing performance metrics and dashboards
  • Correlating logs and events with metrics for troubleshooting

Operate and Monitor - Logging with AWS CloudWatch Logs

  • Collecting and ingesting application logs from various sources
  • Filtering and analyzing logs for root cause analysis
  • Integrating CloudWatch Logs with other services for further analysis
  • Creating and routing events based on triggers (e.g., resource state changes, alarms)
  • Integrating events with other AWS services for automated actions.
    Implementing incident management workflow with CloudWatch Events

Operate and Monitor - Hands on Labs

  • Monitor a sample application with CloudWatch metrics, logs, and events
  • Create alarms and automate actions based on monitoring data
  • Simulate and troubleshoot application issues using CloudWatch tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cloud Computing

  • Key concepts and benefits of cloud computing
  • Differentiating between public, private, and hybrid clouds
  • Introduction to AWS as a public cloud provider

Introduction to AWS Architecture

  • Core AWS components: regions, Availability Zones, and security groups
  • Understanding VPCs (Virtual Private Clouds) and subnets
  • AWS Identity and Access Management (IAM) basics

Introduction of AWS Compute Services

  • Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
  • Amazon Lambda: Serverless compute service for running code without managing servers
  • Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
  • Hands-on: Launching an EC2 instance and exploring its console
  • Hands-on: Creating a simple Lambda function

Introduction of AWS Storage Service

  • Amazon S3: Object storage for any type of data, scalable and durable
  • Amazon EBS: Block storage for EC2 instances, providing persistent volumes
  • Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
  • Amazon DynamoDB: NoSQL database for flexible, scalable data storage
  • Containerization with Docker and Amazon ECS

Introduction of AWS Networking Services

  • VPC (Virtual Private Cloud) creation and management
  • Subnets and network security groups
  • Internet Gateways and NAT Gateways for outbound traffic
  • Route 53 for domain name registration and DNS management

Introduction of AWS Security Services

  • IAM basics: Access policies, roles, and users
  • AWS Security Groups and network access control
  • Amazon Cognito: User authentication and authorization
  • Amazon Key Management Service (KMS): Secure storage and management of encryption keys

Introduction of AWS Monitoring

  • Amazon CloudWatch for monitoring metrics and logs
  • CloudTrail for auditing AWS API calls
  • Amazon CloudFormation for infrastructure as code (IaC)
  • Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

DevOps on AWS -  Introduction to DevOps and GitHub

  • DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
  • Integrating GitHub with AWS for seamless developer workflows
  • Overview of relevant AWS services and GitHub Actions
  • Hands-on: Setting up a GitHub repository for DevOps with AWS

DevOps on AWS -  Version Control with GitHub

  • Managing Git repositories effectively in GitHub
  • Branching strategies and collaboration best practices
  • Utilizing Pull Requests for code review and merge control
  • Integrating CodeCommit with GitHub (optional)
  • Hands-on: Creating a repository, pushing code, managing branches in GitHub

DevOps on AWS -  Continuous Integration with GitHub Actions

  • Creating automated workflows for builds and tests within GitHub Actions
  • Configuring build steps, dependencies, and environment variables
  • Triggering workflows automatically on Git events (push, pull request)
  • Integrating Actions with CodeBuild or other services
  • Hands-on: Building a sample application with GitHub Actions

DevOps on AWS -  Continuous Delivery with GitHub Actions and AWS

  • Designing and building pipelines for deployment automation utilizing Actions
  • Defining stages for code commit, build, test, and deploy using workflows
  • Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
  • Managing approvals and deployments to various environments (staging, production)
  • Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

DevOps on AWS -  Continuous Testing with GitHub Actions and AWS

  • Unit testing and integration testing within Actions workflows
  • Utilizing AWS services like CodeBuild and Lambda for test execution
  • Implementing test coverage and reporting within the pipeline
  • Integration with CodePipeline for advanced testing scenarios
  • Hands-on: Setting up basic unit tests and integrating them with the Actions pipelin

DevOps on AWS -  Infrastructure as Code (IaC) with Terraform

  • Writing infrastructure templates in YAML or JSON and storing them in GitHub
  • Defining resources like EC2 instances, VPCs, and security groups
  • Utilizing Terraform
  • Hands-on: Creating a basic Terraform template in GitHub and deploying it

DevOps on AWS - Continuous Deployment with Advanced Strategies

  • Implementing GitOps and feature flags using GitHub Actions
  • Utilizing canary deployments and A/B testing for safe rollouts
  • Leveraging blue-green deployments for controlled releases
  • Hands-on: Implementing a canary deployment using Actions and AWS services

DevOps on AWS - Containerization with Docker and ECS

  • Understanding containerization benefits and Docker basics
  • Building and managing Docker containers within GitHub repositories
  • Deploying containerized applications to Amazon ECS
  • Scaling and managing containerized applications efficiently
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

DevOps on AWS - Serverless Architecture with AWS Lambda or EKS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications with GitHub
  • Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3

Operate and Monitor - Application Monitoring with AWS CloudWatch

  • Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
  • Visualizing performance metrics and dashboards
  • Correlating logs and events with metrics for troubleshooting

Operate and Monitor - Logging with AWS CloudWatch Logs

  • Collecting and ingesting application logs from various sources
  • Filtering and analyzing logs for root cause analysis
  • Integrating CloudWatch Logs with other services for further analysis
  • Creating and routing events based on triggers (e.g., resource state changes, alarms)
  • Integrating events with other AWS services for automated actions
  • Implementing incident management workflow with CloudWatch Events

Operate and Monitor - Hands on Labs

  • Monitor a sample application with CloudWatch metrics, logs, and events
  • Create alarms and automate actions based on monitoring data
  • Simulate and troubleshoot application issues using CloudWatch tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

DevOps on AWS -  Introduction to DevOps and GitHub

  • DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
  • Integrating GitHub with AWS for seamless developer workflows
  • Overview of relevant AWS services and GitHub Actions
  • Hands-on: Setting up a GitHub repository for DevOps with AWS

DevOps on AWS -  Version Control with GitHub

  • Managing Git repositories effectively in GitHub
  • Branching strategies and collaboration best practices
  • Utilizing Pull Requests for code review and merge control
  • Integrating CodeCommit with GitHub (optional)
  • Hands-on: Creating a repository, pushing code, managing branches in GitHub

DevOps on AWS -  Continuous Integration with GitHub Actions

  • Creating automated workflows for builds and tests within GitHub Actions
  • Configuring build steps, dependencies, and environment variables
  • Triggering workflows automatically on Git events (push, pull request)
  • Integrating Actions with CodeBuild or other services
  • Hands-on: Building a sample application with GitHub Actions

DevOps on AWS -  Continuous Delivery with GitHub Actions and AWS

  • Designing and building pipelines for deployment automation utilizing Actions
  • Defining stages for code commit, build, test, and deploy using workflows
  • Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
  • Managing approvals and deployments to various environments (staging, production)
  • Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

DevOps on AWS -  Continuous Testing with GitHub Actions and AWS

  • Unit testing and integration testing within Actions workflows
  • Utilizing AWS services like CodeBuild and Lambda for test execution
  • Implementing test coverage and reporting within the pipeline
  • Integration with CodePipeline for advanced testing scenarios
  • Hands-on: Setting up basic unit tests and integrating them with the Actions pipeline

DevOps on AWS -  Infrastructure as Code (IaC) with Terraform

  • Writing infrastructure templates in YAML or JSON and storing them in GitHub
  • Defining resources like EC2 instances, VPCs, and security groups
  • Utilizing Terraform
  • Hands-on: Creating a basic Terraform template in GitHub and deploying it

DevOps on AWS - Continuous Deployment with Advanced Strategies

  • Implementing GitOps and feature flags using GitHub Actions
  • Utilizing canary deployments and A/B testing for safe rollouts
  • Leveraging blue-green deployments for controlled releases
  • Hands-on: Implementing a canary deployment using Actions and AWS services

DevOps on AWS - Containerization with Docker and ECS

  • Understanding containerization benefits and Docker basics
  • Building and managing Docker containers within GitHub repositories
  • Deploying containerized applications to Amazon ECS
  • Scaling and managing containerized applications efficiently
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

DevOps on AWS - Serverless Architecture with AWS Lambda or EKS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications with GitHub
  • Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Cloud Computing

  • Key concepts and benefits of cloud computing
  • Differentiating between public, private, and hybrid clouds
  • Introduction to AWS as a public cloud provider

Introduction to AWS Architecture

  • Core AWS components: regions, Availability Zones, and security groups
  • Understanding VPCs (Virtual Private Clouds) and subnets
  • AWS Identity and Access Management (IAM) basics

Introduction of AWS Compute Services

  • Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
  • Amazon Lambda: Serverless compute service for running code without managing servers
  • Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
  • Hands-on: Launching an EC2 instance and exploring its console
  • Hands-on: Creating a simple Lambda function

Introduction of AWS Storage Service

  • Amazon S3: Object storage for any type of data, scalable and durable
  • Amazon EBS: Block storage for EC2 instances, providing persistent volumes
  • Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
  • Amazon DynamoDB: NoSQL database for flexible, scalable data storage
  • Containerization with Docker and Amazon ECS

Introduction of AWS Networking Services

  • VPC (Virtual Private Cloud) creation and management
  • Subnets and network security groups
  • Internet Gateways and NAT Gateways for outbound traffic
  • Route 53 for domain name registration and DNS management

Introduction of AWS Security Services

  • IAM basics: Access policies, roles, and users
  • AWS Security Groups and network access control
  • Amazon Cognito: User authentication and authorization
  • Amazon Key Management Service (KMS): Secure storage and management of encryption keys

Introduction of AWS Monitoring

  • Amazon CloudWatch for monitoring metrics and logs
  • CloudTrail for auditing AWS API calls
  • Amazon CloudFormation for infrastructure as code (IaC)
  • Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

DevOps on AWS -  Introduction to DevOps and GitHub

  • DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
  • Integrating GitHub with AWS for seamless developer workflows
  • Overview of relevant AWS services and GitHub Actions
  • Hands-on: Setting up a GitHub repository for DevOps with AWS

DevOps on AWS -  Version Control with GitHub

  • Managing Git repositories effectively in GitHub
  • Branching strategies and collaboration best practices
  • Utilizing Pull Requests for code review and merge control
  • Integrating CodeCommit with GitHub (optional)
  • Hands-on: Creating a repository, pushing code, managing branches in GitHub

DevOps on AWS -  Continuous Integration with GitHub Actions

  • Creating automated workflows for builds and tests within GitHub Actions
  • Configuring build steps, dependencies, and environment variables
  • Triggering workflows automatically on Git events (push, pull request)
  • Integrating Actions with CodeBuild or other services
  • Hands-on: Building a sample application with GitHub Actions

DevOps on AWS -  Continuous Delivery with GitHub Actions and AWS

  • Designing and building pipelines for deployment automation utilizing Actions
  • Defining stages for code commit, build, test, and deploy using workflows
  • Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
  • Managing approvals and deployments to various environments (staging, production)
  • Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

DevOps on AWS -  Continuous Testing with GitHub Actions and AWS

  • Unit testing and integration testing within Actions workflows
  • Utilizing AWS services like CodeBuild and Lambda for test execution
  • Implementing test coverage and reporting within the pipeline
  • Integration with CodePipeline for advanced testing scenarios
  • Hands-on: Setting up basic unit tests and integrating them with the Actions pipelin

DevOps on AWS -  Infrastructure as Code (IaC) with Terraform

  • Writing infrastructure templates in YAML or JSON and storing them in GitHub
  • Defining resources like EC2 instances, VPCs, and security groups
  • Utilizing Terraform
  • Hands-on: Creating a basic Terraform template in GitHub and deploying it

DevOps on AWS - Continuous Deployment with Advanced Strategies

  • Implementing GitOps and feature flags using GitHub Actions
  • Utilizing canary deployments and A/B testing for safe rollouts
  • Leveraging blue-green deployments for controlled releases
  • Hands-on: Implementing a canary deployment using Actions and AWS services

DevOps on AWS - Containerization with Docker and ECS

  • Understanding containerization benefits and Docker basics
  • Building and managing Docker containers within GitHub repositories
  • Deploying containerized applications to Amazon ECS
  • Scaling and managing containerized applications efficiently
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

DevOps on AWS - Serverless Architecture with AWS Lambda or EKS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications with GitHub
  • Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3

Operate and Monitor - Application Monitoring with AWS CloudWatch

  • Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
  • Visualizing performance metrics and dashboards
  • Correlating logs and events with metrics for troubleshooting

Operate and Monitor - Logging with AWS CloudWatch Logs

  • Collecting and ingesting application logs from various sources
  • Filtering and analyzing logs for root cause analysis
  • Integrating CloudWatch Logs with other services for further analysis
  • Creating and routing events based on triggers (e.g., resource state changes, alarms)
  • Integrating events with other AWS services for automated actions
  • Implementing incident management workflow with CloudWatch Events

Operate and Monitor - Hands on Labs

  • Monitor a sample application with CloudWatch metrics, logs, and events
  • Create alarms and automate actions based on monitoring data
  • Simulate and troubleshoot application issues using CloudWatch tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

DevOps on AWS -  Introduction to DevOps and GitHub

  • DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
  • Integrating GitHub with AWS for seamless developer workflows
  • Overview of relevant AWS services and GitHub Actions
  • Hands-on: Setting up a GitHub repository for DevOps with AWS

DevOps on AWS -  Version Control with GitHub

  • Managing Git repositories effectively in GitHub
  • Branching strategies and collaboration best practices
  • Utilizing Pull Requests for code review and merge control
  • Integrating CodeCommit with GitHub (optional)
  • Hands-on: Creating a repository, pushing code, managing branches in GitHub

DevOps on AWS -  Continuous Integration with GitHub Actions

  • Creating automated workflows for builds and tests within GitHub Actions
  • Configuring build steps, dependencies, and environment variables
  • Triggering workflows automatically on Git events (push, pull request)
  • Integrating Actions with CodeBuild or other services
  • Hands-on: Building a sample application with GitHub Actions

DevOps on AWS -  Continuous Delivery with GitHub Actions and AWS

  • Designing and building pipelines for deployment automation utilizing Actions
  • Defining stages for code commit, build, test, and deploy using workflows
  • Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
  • Managing approvals and deployments to various environments (staging, production)
  • Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

DevOps on AWS -  Continuous Testing with GitHub Actions and AWS

  • Unit testing and integration testing within Actions workflows
  • Utilizing AWS services like CodeBuild and Lambda for test execution
  • Implementing test coverage and reporting within the pipeline
  • Integration with CodePipeline for advanced testing scenarios
  • Hands-on: Setting up basic unit tests and integrating them with the Actions pipeline

DevOps on AWS -  Infrastructure as Code (IaC) with Terraform

  • Writing infrastructure templates in YAML or JSON and storing them in GitHub
  • Defining resources like EC2 instances, VPCs, and security groups
  • Utilizing Terraform
  • Hands-on: Creating a basic Terraform template in GitHub and deploying it

DevOps on AWS - Continuous Deployment with Advanced Strategies

  • Implementing GitOps and feature flags using GitHub Actions
  • Utilizing canary deployments and A/B testing for safe rollouts
  • Leveraging blue-green deployments for controlled releases
  • Hands-on: Implementing a canary deployment using Actions and AWS services

DevOps on AWS - Containerization with Docker and ECS

  • Understanding containerization benefits and Docker basics
  • Building and managing Docker containers within GitHub repositories
  • Deploying containerized applications to Amazon ECS
  • Scaling and managing containerized applications efficiently
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

DevOps on AWS - Serverless Architecture with AWS Lambda or EKS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications with GitHub
  • Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cloud Computing

  • Explain the concept of cloud computing.
  • Discuss its benefits, such as scalability, flexibility, and cost-effectiveness.
  • Highlight the differences between on-premises, cloud, and hybrid models.
  • Explain the different cloud deployment models (public, private, hybrid).
  • Explore the three main service models (IaaS, PaaS, SaaS) and their characteristics.

Google Cloud Platform Overview

  • Discuss the history and evolution of Google Cloud Platform.
  • Highlight its key services and features.
  • Introduce major GCP services, such as Compute Engine, Cloud Storage, Cloud SQL, Cloud Functions, and more.
  • Provide examples of how these services are used in real-world applications.
  • Hands-on Lab: Setting up a GCP project and navigating the console (30 min)

Compute Engine

  • Define virtual machines and their benefits.
  • Compare VMs to physical machines.
  • Guide participants through creating a VM instance in the console.
  • Cover selecting machine type, configuring boot disk, and setting network access.
  • Demonstrate connecting to the VM instance and managing its resources.
  • Explain scaling options for VMs (up or down) based on resource needs.
  • Introduce basic networking concepts in GCP and how VMs connect to networks.
  • Hands-on Lab: Deploying and managing a VM (60 min)

Cloud Storage

  • Object Storage Basics
  • Uploading and Downloading Data
  • Access Control and Lifecycle Management
  • Hands-on Lab: Uploading and downloading data to different storage classes (45 min)

Cloud SQL

  • Introduction to Cloud SQL
  • Choosing the Right Instance Type
  • Creating and Managing Cloud SQL Instances
  • Exploring Cloud SQL features
  • Hands-on Lab: Creating a Cloud SQL database instance and connecting to it from an application

Cloud Networking

  • Introduction to VPC
  • Managing VPC and Firewall
  • Deploying Firewall and Network Tags
  • Best practices of GCP Networking

Monitoring & Billing

  • Using Cloud Monitoring
  • Understanding GCP Billing

Security & Compliance

  • Security Best Practices in GCP
  • Understanding IAM
  • GCP Compliance Offerings
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cloud Computing

  • Explain the concept of cloud computing.
  • Discuss its benefits, such as scalability, flexibility, and cost-effectiveness.
  • Highlight the differences between on-premises, cloud, and hybrid models.
  • Explain the different cloud deployment models (public, private, hybrid).
  • Explore the three main service models (IaaS, PaaS, SaaS) and their characteristics.

Google Cloud Platform Overview

  • Discuss the history and evolution of Google Cloud Platform.
  • Highlight its key services and features.
  • Introduce major GCP services, such as Compute Engine, Cloud Storage, Cloud SQL, Cloud Functions, and more.
  • Provide examples of how these services are used in real-world applications.
  • Hands-on Lab: Setting up a GCP project and navigating the console (30 min)

Compute Engine

  • Define virtual machines and their benefits.
  • Compare VMs to physical machines.
  • Guide participants through creating a VM instance in the console.
  • Cover selecting machine type, configuring boot disk, and setting network access.
  • Demonstrate connecting to the VM instance and managing its resources.
  • Explain scaling options for VMs (up or down) based on resource needs.
  • Introduce basic networking concepts in GCP and how VMs connect to networks.
  • Hands-on Lab: Deploying and managing a VM (60 min)

Cloud Storage

  • Object Storage Basics
  • Uploading and Downloading Data
  • Access Control and Lifecycle Management
  • Hands-on Lab: Uploading and downloading data to different storage classes (45 min)

Cloud SQL

  • Introduction to Cloud SQL
  • Choosing the Right Instance Type
  • Creating and Managing Cloud SQL Instances
  • Exploring Cloud SQL features
  • Hands-on Lab: Creating a Cloud SQL database instance and connecting to it from an application

Cloud Networking

  • Introduction to VPC
  • Managing VPC and Firewall
  • Deploying Firewall and Network Tags
  • Best practices of GCP Networking

Monitoring & Billing

  • Using Cloud Monitoring
  • Understanding GCP Billing

Security & Compliance

  • Security Best Practices in GCP
  • Understanding IAM
  • GCP Compliance Offerings
  • Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to AWS and Cloud Migration

  • Introduction to cloud computing concepts and benefits
  • AWS overview and key services relevant to migration
  • Understanding migration drivers, challenges, and success factors
  • Identifying different migration strategies (rehost, refactor, replatform, rebuild)

AWS Essentials for Migration

  • Hands-on labs: Creating an AWS account, managing IAM roles and users
  • S3 object storage for data migration and archiving
  • EC2 instances for application hosting and scaling
  • VPCs for creating secure and isolated network environment

Application Readiness Assessment

  • Techniques for evaluating application dependencies and architecture
  • Identifying potential migration risks and mitigation strategies
  • Tools for dependency analysis and code refactoring

Migration Planning and Cost Estimation

  • Developing a migration roadmap and timeline
  • Estimating migration costs using AWS pricing tools
  • Risk mitigation strategies and rollback plans

Advanced Migration Techniques

  • Code refactoring for cloud-native architecture
  • Database migration strategies and tools
  • Containerization with Docker and Amazon ECS

Monitoring and Optimization

  • CloudWatch, CloudTrail, and other monitoring tools for performance and cost insights
  • Optimization techniques for cost savings and resource efficiency

Security Best Practices for Cloud Applications

  • IAM policies and access control mechanisms
  • Security considerations for data encryption and network security
  • AWS Shield for DDoS protection
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Cloud Computing

  • Key concepts and benefits of cloud computing
  • Differentiating between public, private, and hybrid clouds
  • Introduction to AWS as a public cloud provider

Introduction to AWS Architecture

  • Core AWS components: regions, Availability Zones, and security groups
  • Understanding VPCs (Virtual Private Clouds) and subnets
  • AWS Identity and Access Management (IAM) basics

Introduction of AWS Compute Services

  • Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
  • Amazon Lambda: Serverless compute service for running code without managing servers
  • Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
  • Hands-on: Launching an EC2 instance and exploring its console
  • Hands-on: Creating a simple Lambda function

Introduction of AWS Storage Service

  • Amazon S3: Object storage for any type of data, scalable and durable
  • Amazon EBS: Block storage for EC2 instances, providing persistent volumes
  • Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
  • Amazon DynamoDB: NoSQL database for flexible, scalable data storage
  • Containerization with Docker and Amazon ECS

Introduction of AWS Networking Services

  • VPC (Virtual Private Cloud) creation and management
  • Subnets and network security groups
  • Internet Gateways and NAT Gateways for outbound traffic
  • Route 53 for domain name registration and DNS management

Introduction of AWS Security Services

  • IAM basics: Access policies, roles, and users
  • AWS Security Groups and network access control
  • Amazon Cognito: User authentication and authorization
  • Amazon Key Management Service (KMS): Secure storage and management of encryption keys

Introduction of AWS Monitoring

  • Amazon CloudWatch for monitoring metrics and logs
  • CloudTrail for auditing AWS API calls
  • Amazon CloudFormation for infrastructure as code (IaC)
  • Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

Deployment Application on AWS

  • Building serverless functions for event-driven applications in GitHub
  • Utilizing triggers and scaling in Lambda functions or EKS
  • Integrating Lambda with other services like S3 and DynamoDB
  • Best practices for building and deploying serverless applications in AWS
  • Hands-on: Creating a simple Lambda function  and connecting it to S3

Operate and Monitor - Application Monitoring with AWS CloudWatch

  • Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
  • Visualizing performance metrics and dashboards
  • Correlating logs and events with metrics for troubleshooting

Operate and Monitor - Logging with AWS CloudWatch Logs

  • Collecting and ingesting application logs from various sources
  • Filtering and analyzing logs for root cause analysis
  • Integrating CloudWatch Logs with other services for further analysis
  • Creating and routing events based on triggers (e.g., resource state changes, alarms)
  • Integrating events with other AWS services for automated actions
  • Implementing incident management workflow with CloudWatch Events

Operate and Monitor - Hands on Labs

  • Monitor a sample application with CloudWatch metrics, logs, and events
  • Create alarms and automate actions based on monitoring data
  • Simulate and troubleshoot application issues using CloudWatch tools
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
*Minimum investment to start the training is 30 pax (Rp58,5 million)
Per-Headcount Include Tax
RpXXX
50 participants*
Rp XXX
Total Class
XXX
Total Learners
XXX
Duration
XXX
*Minimum investment to start the training is 30 pax (Rp58,5 million)
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of Big Data and Pyspark

  • Big Data and Pyspark knowledge refreshment
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

Environment and Machine Setup for Pyspark

  • Local machine set up: Python setup with Spark
  • Local virtual box set up
  • Cluster set up
  • Spark configuration setup for Distribute Processing Performance Tuning

Data Preparation with Pyspark

  • Fundamental Spark DataFrame
  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Spark Performance Tuning

  • Fundamental Regression, Classification, and Time Series Analysis
  • Regression algorithm: Linear Regression, Regularization (L1 lasso, L2 Ridge, and elasticnet)
  • Classification Algorithm: Decision tree, bagging, random forest, boosting and gradient boosted trees, and naïve bayes
  • Advanced supervised learning real use case and practice with Python (E2E application)

Machine Learning with MLlib Pyspark

  • Unsupervised learning with pyspark (K-Means Clustering)
  • Supervised learning with pyspark (Regression & Classification)
  • Collaborative Filtering for Recomender System wih Pyspark
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

EDA with Python

  • EDA with Python
  • Exploring categorical data with Python
  • Exploring numerical data with Python
  • Basic statistical concepts: histogram, variance, and data distribution
  • Communicating results of exploratory data analysis

Statistical Inference

Classical Statistical Tests

  • Hypothesis testing and confidence intervals
  • Performing classical statistical tests in Python

Randomized Experiments and Hypothesis Testing

  • Creating, organizing, and conducting randomized experiments (A/B testing)
  • Hypothesis testing to determine the impact of experiments
  • Conducting hypothesis tests with Python

Bootstrap & Confidence Interval

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples with replacement from data distribution (bootstrapping)
  • Determining confidence intervals from bootstrap samples

Permutation Testing

  • Limitations of classical statistical tests & the benefits of simulating with data
  • Drawing random samples without replacement from data distribution (permutation)
  • Conducting hypothesis tests with permutation sampling

Introduction to Modeling

Linear Regression

  • Basic concepts of linear regression
  • Applying linear regression in Python
  • Inference with linear regression
  • Selecting a good linear regression model
  • Case Study: Predicting Default Rate with Macroeconomic Data

Logistic Regression

  • Predicting the probability of an event with logistic regression
  • Applying logistic regression in Python
  • Inference with logistic regression
  • Selecting a good logistic regression model
  • Case Study: Creating a Propensity to Buy Model for Retail Customers
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database, unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and RJupyter Notebook

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science
  • Practice of Good Scripting Practices in Python

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python
  • Creating Data Visualizations with Python
  • Creating Data Visualizations with Matplotlib

Creating Interactive Data Visualizations with PyGWalker

  • Structured file/data preparation with Pyspark
  • Semi-structured file/data preparation with Pyspark
  • Unstructured file/data preparation with Pyspark

Data Science Projects with Python

Data Science Case Study with Python

  • Case Study using Lending Club data
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Introduction to Data Science

Data Around Us

  • Four types of data (nominal, ordinal, ratio, interval)
  • Electronic data formats (CSV, JSON, statistical formats, Database,
  • unstructured data)
  • Discovering data in the financial industry environment

Introduction to Data Science

  • Defining Data Science
  • Daily Tasks of a Data Scientist
  • Examples of implementing data science in the financial industry
  • Examples of implementing data science outside the financial industry

Methodology and Tools for Data Science

Proses Cross-industry Standard Process for Data Mining (CRISP-DM)

  • Understanding Business Issues (Business Understanding)
  • Understanding Data (Data Understanding)
  • Preparing Data (Data Preparation)
  • Data Modeling (Modeling)
  • Model Evaluation (Model Evaluation)
  • Using Models (Model Deployment)

Introduction to Tools for Data Science

  • Spreadsheet
  • Database & SQL
  • High-level programming languages - Python and R
  • Jupyter Notebook

SQL for Data Science

SQL for Data Science

  • Connecting DBeaver with SQLite Database
  • Querying data with SQL: SELECT, FROM GROUP BY, WHERE, HAVING
  • Advanced SQL commands: WITH clause, window functions

Python for Data Science

Introduction to Python

  • Using Python for Data Science
  • Data Types and Operators in Python
  • Python Data Structures
  • Introduction to Object-Oriented Programming (OOP) in Python

Compiling Python Programs

  • Python Program Control Flow
  • Creating Python Functions
  • Python Libraries and Modules for Data Science

Understanding Data with Python

  • Opening Data in Python with Pandas Library
  • Selecting and Inspecting Data in Python
  • Creating Data Summaries (Summary Statistics) in Python

Preparing Data with Python

  • Raw Data and Clean Data
  • Finding and Cleaning Duplicate Data with Python
  • Finding and Cleaning Missing Data with Python
  • Cleaning Text Data in Python
  • Cleaning Datetime Data in Python
  • Creating Data Categories (binning) in Python

Creating Data Visualizations with Python

  • Creating Data Visualizations with Matplotlib

Machine Learning with Python

Introduction to Machine Learning and Linear Regression

  • Understanding Machine Learning (ML)
  • Three types of ML models: Supervised Learning, Unsupervised Learning,
  • Reinforcement Learning
  • CRISP-DM Process in Machine Learning Projects
  • Linear Regression with Python
  • Evaluation metrics for linear regression models
  • Evaluating Linear Regression Models with Cross-Validation

Classification Model with Logistic Regression

  • Logistic Regression and Weight-of-Evidence Transformation
  • Logistic Regression with Python
  • Selecting Logistic Regression Model with Stepwise Method
  • Evaluation metrics for logistic regression models
  • Evaluating Logistic Regression Models with Cross-Validation

Unsupervised Learning

  • Basic concepts of unsupervised learning
  • K-Means clustering as an example of unsupervised learning
  • Evaluation metrics for k-means clustering models
  • Customer segmentation case study

Classification Model with Tree-Based Methods

  • Creating a Classification Model with Decision Tree Algorithm
  • Creating a Classification Model with Random Forest Algorithm
  • Creating a Classification Model with XGBoost Algorithm

Integrated data science project

  • Data Science Case Study with SQL, Python, and Machine Learning
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title
1. Best Hands-on Big Data Practices with PySpark & Spark Tuning
TOPIC

Fundamental of scalable data science

  • Fundamental of big data (The 5 V's : velocity, volume, value, and variety)
  • Technology (tools, system, etc) used in big data processing and analysis
  • Intro to Apache Spark and Pyspark

ML Model Versioning

  • Machine Learning Pipeline
  • Save & Load Model as pickle object

Model Optimization

  • Fundamental of Hyperparameter Tuning
  • Fundamental of Cross validation and Gridsearch
  • Hyperparameter tuning application on Regression and Classification

Deep Learning Algorithm

  • Fundamental Neural network and Deep Learning
  • Understanding deep learning architecture
  • Build and train deep learning model
  • Improve deep learning model (hyperparameter tuning, regularization, and optimization)
  • Convolutional neural network
  • Sequence Model
  • Advanced deep learning real use case and practice with Python (E2E application)

Git & Github Fundamental

  • Understanding Git & Github application in industry
  • Install github and practice some basic git command, and use github dekstop
  • Create and close git project
  • Learning how to push file to github as collaboration

Model Deployment

  • Streamlit application fundamental (concept and code practice)
  • Github and streamlit collaboration
  • Practice to create simple ML model deployment using Regression/Classification usecase

Advanced Data Science Project (Capstone)

  • Implement CRISP-DM framework
  • Business Understanding: Understanding business requirement of a usecase, and define what solution suitable to it (unsupervised learning, supervised learning, or deep learning)
  • Data Understanding: Exploring what data providing by client, understanding the context
  • Data Preparation: Start from creating a comprehensive SQL query using CTE, preprocessing data with Python (data train and data predict preparation for deployment)
  • Modeling: Creating a comprehensive ML pipeline, including feature engineering, fitting model, model evaluation, feature importance, and prediction scenario
  • Evaluation: Creating an automate self evaluate ML capability that enabling for deployment purposes
  • Deployment: Creating a deployment system that connected between python, github, and streamlit platform
TOTAL INVESTMENT
Price per Learner
(Include Tax)
Total Price* per Training Title

Why RevoU Will Be Your Best Skill-Based Training Partner

Experienced in Partnering with BRI Corporate University

We have previously collaborated with BRI, fully understanding your needs by customizing the modules, providing the best instructors, and more.

Skill-Based Projects & Training

Engage in learning with 70% hands-on practice, 20% mentoring, and 10% lectures that directly apply to your company’s current challenges.

Access to the Widest Reach of Carefully Selected Instructors in Financial & Banking Industry

Your team will be coached with live & interactive lectures by experts who work in financial & banking industries

A Dedicated Account Manager to Oversee All Trainings

Enjoy the simplicity of coordinating your trainings with a dedicated Account Manager, eliminating the hassle of juggling multiple contacts.

Iterative Approach to Training

Your feedback is our priority. Expect agile training sessions with improvements after each session to ensure a great learning experience!

Pre-Post Tests To Measure Team’s Improvement

Assess your team’s skills with a pre-test before the training begins and a post-test afterward to ensure every team member’s skills have improved!

Engage in Live Lessons with Top Practitioners from Leading Financial & Banking Companies

Marthin Rajagukguk
AVP - Cyber Security Advisory at CIMB Niaga
System information & removal Instructor
10 years experience
Arief Noorman
Head of Cyber Risk at Allo Bank
Information Security & Security Ops
22 years experience
Nicholas Vidya
AVP - Product and System Quality Manager at UOB
Data Science & Data Visualization
17 years experience
Rahmat Hidayatullah
Principal Data Scientist at
PT. XL Axiata Tbk
Data Science & Data Visualization
13 years experience
Herfan Yusano
DevOps Technical Lead at Ula
System Installation & Removal
12 years experience
Muhammad Luqman
Security Engineering Lead at Gojek
Information Security & IT infrastructure
8 years experience
Akmal Fadhlurrahman
Data Scientist at UOB
Machine Learning & Data Science
6 years experience
Hervind Philippe
Full Stack Engineer at Cyan
Machine Learning & Data Science
6 years experience

"Pesertanya gak bosan. Having fun. Dapat tugas juga tapi tidak jadi beban. Banyak hal-hal update dan kemasannya luar biasa. Peserta mendapat pengetahuan dan experience yang berbeda."

dr. Siti Nadia Tarmizi, M.Epid
Kepala Biro Komunikasi dan Pelayanan Publik di Kemenkes RI

"Generasi yang senior sering dianggap gaptek, susah menangkap materi semacam ini. Tapi asal dijelaskan dengan baik, dengan menarik, mungkin karena expertise dari kawan2 di RevoU juga pemateri Pak Arie keren banget sehingga kami bisa menangkap lebih mudah"

Ismid
Kepala Tim Pengelolaan Uang Rupiah KPw di Bank Indonesia Jatim

“Yang paling berkesan tuh study case. RevoU mengusulkan beberapa topik data yang masih terkait industri banking, seperti di kelompokku ada Churn Customer dalam industri perbankan”

Tsaqif Alfatan Nugrah
Data Analyst at BRI

“Membantu sekali memiliki mentor yang dapat memandu tugas, bukan lagi diserahkan kepada pemateri seperti di program lainnya. Selain itu, penilaian peers di tim juga sangat bermanfaat untuk peningkatan di kolaborasi berikutnya"

Marvin Mahadharma Muditajaya
Head of Telkomsel tSurvey at Telkomsel

“Kolaborasi dengan RevoU sangat mudah karena semuanya detail, tertata, dan gerak cepat banget terhadap situasi. Yang terpenting adalah materi speakernya +100, alias lengkap! Thank you RevoU!”

Nailah Rahmah
Merchant Engagement at Blibli

"The material is very related to the way of working that we are doing"

Christian Arry
Customer Facing & Internal Product Management Group Head at Sinarmas

"Paragon cukup agile, dan ternyata cocok juga sama RevoU. Thank you RevoU udah agile dalam craft modul yang dilaksanakan dan instructornya bisa memfasilitasi teman-teman untuk bertanya dan konsultasi"

Yubi
Human Resource at ParagonCorp

Banking Digital Transformation Journey with RevoU Corporate Training

BRI

A 3-month Data Analytics Corporate Training program designed for the members of the BRI IT & Data Analytics Operations team

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Bank Nobu

A Data Analytics Webinar designed for the Bank Nobu’s Branch Managers and Sales & Marketing Team

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Bank Indonesia

A 5-day Data Analytics Corporate Training program designed for Bank Indonesia Sulawesi Tengah’s Analytics Team.

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Hear Why They Love Learning With Us

"Generasi yang senior sering dianggap gaptek, susah menangkap materi semacam ini. Tapi asal dijelaskan dengan baik, dengan menarik, mungkin karena expertise dari kawan-kawan di RevoU juga pemateri Pak Arie keren banget sehingga kami bisa menangkap lebih mudah"

Ismid
Kepala Tim Pengelolaan Uang Rupiah KPw di Bank Indonesia Jatim

"The material is very related to the way of working that we are doing"

Christian Arry
Customer Facing & Internal Product Management Group Head at Sinarmas

“Yang paling berkesan tuh study case. RevoU mengusulkan beberapa topik data yang masih terkait industri banking, seperti di kelompokku ada Churn Customer dalam industri perbankan”

Tsaqif Alfatan Nugrah
Data Analyst at BRI

"Pesertanya gak bosan. Having fun. Dapat tugas juga tapi tidak jadi beban. Banyak hal-hal update dan kemasannya luar biasa. Peserta mendapat pengetahuan dan experience yang berbeda."

dr. Siti Nadia Tarmizi, M. Epid
Kepala Biro Komunikasi dan Pelayanan Publik di Kemenkes RI

“Membantu sekali memiliki mentor yang dapat memandu tugas, bukan lagi diserahkan kepada pemateri seperti di program lainnya. Selain itu, penilaian peers di tim juga sangat bermanfaat untuk peningkatan di kolaborasi berikutnya"

Marvin Mahadharma Muditajaya
Head of Telkomsel tSurvey at Telkomsel

“Kolaborasi dengan RevoU sangat mudah karena semuanya detail, tertata, dan gerak cepat banget terhadap situasi. Yang terpenting adalah materi speakernya +100, alias lengkap! Thank you RevoU!”

Nailah Rahmah
Merchant Engagement at Blibli

"Paragon cukup agile, dan ternyata cocok juga sama RevoU. Thank you RevoU udah agile dalam craft modul yang dilaksanakan dan instructornya bisa memfasilitasi teman-teman untuk bertanya dan konsultasi"

Yubi
Human Resource at ParagonCorp

"Generasi yang senior sering dianggap gaptek, susah menangkap materi semacam ini. Tapi asal dijelaskan dengan baik, dengan menarik, mungkin karena expertise dari kawan2 di RevoU juga pemateri Pak Arie keren banget sehingga kami bisa menangkap lebih mudah"

Ismid
Kepala Tim Pengelolaan Uang Rupiah KPw di Bank Indonesia Jatim

“Yang paling berkesan tuh study case. RevoU mengusulkan beberapa topik data yang masih terkait industri banking, seperti di kelompokku ada Churn Customer dalam industri perbankan”

Tsaqif Alfatan Nugrah
Data Analyst at BRI

"The material is very related to the way of working that we are doing"

Christian Arry
Customer Facing & Internal Product Management Group Head at Sinarmas

"Pesertanya gak bosan. Having fun. Dapat tugas juga tapi tidak jadi beban. Banyak hal-hal update dan kemasannya luar biasa. Peserta mendapat pengetahuan dan experience yang berbeda."

dr. Siti Nadia Tarmizi, M.Epid
Kepala Biro Komunikasi dan Pelayanan Publik di Kemenkes RI

“Membantu sekali memiliki mentor yang dapat memandu tugas, bukan lagi diserahkan kepada pemateri seperti di program lainnya. Selain itu, penilaian peers di tim juga sangat bermanfaat untuk peningkatan di kolaborasi berikutnya"

Marvin Mahadharma Muditajaya
Head of Telkomsel tSurvey at Telkomsel

“Kolaborasi dengan RevoU sangat mudah karena semuanya detail, tertata, dan gerak cepat banget terhadap situasi. Yang terpenting adalah materi speakernya +100, alias lengkap! Thank you RevoU!”

Nailah Rahmah
Merchant Engagement at Blibli

"Paragon cukup agile, dan ternyata cocok juga sama RevoU. Thank you RevoU udah agile dalam craft modul yang dilaksanakan dan instructornya bisa memfasilitasi teman-teman untuk bertanya dan konsultasi"

Yubi
Human Resource at ParagonCorp
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Fonda Priskilla
Head of BD & Partnerships
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