The course focuses on how Machine Learning (ML) can help enhance business strategy and decision-making.
Objectives of the Course:
- Understand the fundamentals of Business Intelligence and its components.
- Explore the role of Machine Learning in BI.
- Analyze case studies of successful BI implementations with ML.
- Apply ML techniques to solve real-world BI problems.
Grading Policy
- Project: 30%
- Midterm Exam: 30%
- Final Exam: 40%
Week 1: Introduction to Business Intelligence and Machine Learning
- Overview of Business Intelligence
- Role of Machine Learning in BI
- Case Studies: Successful BI implementations with ML
Week 2-4: Data Preprocessing and Exploration for BI
- Data cleaning and preparation
- Exploratory data analysis (EDA) with Python
- Visualization Techniques for Business Data using Google Data Studio
Week 5-6: Supervised Learning for Business Analytics
- Regression analysis for sales forecasting and financial modeling
- Classification techniques for customer segmentation and churn prediction
Week 7-8: Unsupervised Learning in BI
- Clustering for market segmentation
- Association rules for market basket analysis
Week 9-10: Advanced Machine Learning Techniques
- Introduction to neural networks and deep learning in BI
- Reinforcement learning for dynamic pricing strategies
- Natural Language Processing (NLP) for customer feedback analysis
Week 11: Integrating ML Models into Business Processes
- Deployment strategies for ML models
- Ethics and privacy considerations in BI
Week 12-14: Project Presentations
- Group project presentations