| 1 |
Introduction of the syllabus and the rules of the course. |
| 2 |
Introduction to Business Intelligence Overview of Business Intelligence: Definition and Importance Role of BI in Decision Making Introduction to BI Tools |
| 3 |
Data Warehousing Concepts Data Warehousing vs. Databases ETL Processes: Extraction, Transformation, Loading Data Warehouse Architecture |
| 4 |
Data Integration and Quality Importance of Data Quality Data Cleansing Techniques Tools for Data Integration |
| 5 |
Introduction to Data Mining Overview of Data Mining Data Mining Techniques: Classification, Clustering, Regression Tools for Data Mining |
| 6 |
Descriptive and Predictive Analytics Understanding Descriptive Analytics Introduction to Predictive Modeling Applications of Predictive Analytics in Business |
| 7 |
Midterm Exam |
| 8 |
Data Visualization Techniques Principles of Data Visualization Tools for Creating Visualizations (e.g., Tableau, Power BI) Storytelling with Data |
| 9 |
BI Software Applications Overview of Popular BI Tools Hands-On Lab: Using BI Software Practical Application Scenarios |
| 10 |
BI Software Applications Overview of Popular BI Tools Hands-On Lab: Using BI Software Practical Application Scenarios |
| 11 |
Case Studies in Business Intelligence Analyzing Real-Life BI Implementation Case Studies Group Discussions and Presentations |
| 12 |
Ethical Considerations and Future Trends Data Ethics and Compliance Future Trends in Business Intelligence Emerging Technologies (AI, Machine Learning in BI) |
| 13 |
Course Review and Final Project Preparation Review of Key Concepts Guidelines for Final Project Presentation |
| 14 |
Final Term Exam |
