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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.
Our curriculum is tailored to meet global digital skill demands, anchored in the
competencies framework.
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Overview of Deep Learning
Neural Networks Basics
Activation Functions and Forward Propagation
Backward Propagation and Model Training
Applications of Deep Learning in Banking
Credit Scoring and Risk Assessment
Fraud Detection and Prevention
Customer Service Automation
Convolutional Neural Networks (CNNs)
Image Recognition in Banking
Basic of Computer Vision
Image Recognition Application
Recurrent Neural Networks (RNNs)
Transfer Learning and Fine-tuning
Basics of Natural Language Processing
Deep Learning for Natural Languange Processing
Deep Learning Implementation
Deployment Strategies
Model Monitoring and Maintenance
Model Retraining Strategies
Ethical Guidelines in AI
Mitigating Bias in Deep Learning Models
Regulatory Compliance and Data Privacy
Federated Learning: Collaborative Model Training
Transformer Models in Deep Learning
Hands-on Workshop: Implementing Transformer Models
Generative AI: Understanding and Implementing
LLM Generative AI (ChatGPT, Bard, etc)
Capstone Project
Overview of Machine Learning
Overview of Machine Learning application in Banking
Tools in Machine Learning
Exploratory Data Analysis (EDA)
Data Preprocessing Techniques
Practical Session
Supervised Machine Learning
Supervised Machine Learning (Regression vs Classification)
Regression Model
Regression Model
Advanced Supervised Machine Learning Model
Machine Learning Metrics
Model Optimization Strategies and Overfitting/Underfitting
Machine Learning Model Deployment
Capstone Project
Foundations of Unsupervised Learning
Overview of Clustering in Machine Learning
Clustering Algorithm
Dimensionality Reduction
Anomaly Detection
Practical Session
System Recommendation
Capstone Project
Overview of Deep Learning
Neural Networks Basics
Activation Functions and Forward Propagation
Backward Propagation and Model Training
Applications of Deep Learning in Banking
Credit Scoring and Risk Assessment
Fraud Detection and Prevention
Customer Service Automation
Convolutional Neural Networks (CNNs)
Image Recognition in Banking
Basic of Computer Vision
Image Recognition Application
Recurrent Neural Networks (RNNs)
Transfer Learning and Fine-tuning
Basics of Natural Language Processing
Deep Learning for Natural Languange Processing
Deep Learning Implementation
Deployment Strategies
Model Monitoring and Maintenance
Model Retraining Strategies
Ethical Guidelines in AI
Mitigating Bias in Deep Learning Models
Regulatory Compliance and Data Privacy
Federated Learning: Collaborative Model Training
Transformer Models in Deep Learning
Hands-on Workshop: Implementing Transformer Models
Generative AI: Understanding and Implementing
LLM Generative AI (ChatGPT, Bard, etc)
Capstone Project
Overview of Machine Learning
Overview of Machine Learning application in Banking
Tools in Machine Learning
Exploratory Data Analysis (EDA)
Data Preprocessing Techniques
Practical Session
Supervised Machine Learning
Supervised Machine Learning (Regression vs Classification)
Regression Model
Regression Model
Advanced Supervised Machine Learning Model
Machine Learning Metrics
Model Optimization Strategies and Overfitting/Underfitting
Machine Learning Model Deployment
Capstone Project
Foundations of Unsupervised Learning
Overview of Clustering in Machine Learning
Clustering Algorithm
Dimensionality Reduction
Anomaly Detection
Practical Session
System Recommendation
Capstone Project
Understanding Design Patterns
Designing for Scalability and Performance
Designing for Reliability and Availability
Designing for Security and Compliance
Monitoring and Analyzing Performance
Optimizing Compute Resources
Optimizing Storage Resources
Optimizing Network Resources
Securing Compute Resources
Securing Storage Resources
Securing Network Resources
Maintaining Compliance
Understanding Design Patterns
Designing for Scalability and Performance
Designing for Reliability and Availability
Designing for Security and Compliance
Monitoring and Analyzing Performance
Optimizing Compute Resources
Optimizing Storage Resources
Optimizing Network Resources
Securing Compute Resources
Securing Storage Resources
Securing Network Resources
Maintaining Compliance
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
Classical Statistical Tests
Randomized Experiments and Hypothesis Testing
Bootstrap & Confidence Interval
Permutation Testing
Linear Regression
Logistic Regression
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Interactive Data Visualizations with PyGWalker
Data Science Case Study with Python
Data Around Us
Introduction to Data Science
Proses Cross-industry Standard Process for Data Mining (CRISP-DM)
Introduction to Tools for Data Science
SQL for Data Science
Introduction to Python
Compiling Python Programs
Understanding Data with Python
Preparing Data with Python
Creating Data Visualizations with Python
Introduction to Machine Learning and Linear Regression
Classification Model with Logistic Regression
Unsupervised Learning
Classification Model with Tree-Based Methods
We have previously collaborated with BRI, fully understanding your needs by customizing the modules, providing the best instructors, and more.
Engage in learning with 70% hands-on practice, 20% mentoring, and 10% lectures that directly apply to your company’s current challenges.
Your team will be coached with live & interactive lectures by experts who work in financial & banking industries
Enjoy the simplicity of coordinating your trainings with a dedicated Account Manager, eliminating the hassle of juggling multiple contacts.
Your feedback is our priority. Expect agile training sessions with improvements after each session to ensure a great learning experience!
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!
A 3-month Data Analytics Corporate Training program designed for the members of the BRI IT & Data Analytics Operations team
A Data Analytics Webinar designed for the Bank Nobu’s Branch Managers and Sales & Marketing Team
A 5-day Data Analytics Corporate Training program designed for Bank Indonesia Sulawesi Tengah’s Analytics Team.