| 1 |
Introduction to AI: Past, Present, and Future |
| 2 |
Learning from Data: Evolution of Machine Learning |
| 3 |
Foundations & Lifecycle of Data Engineering |
| 4 |
Data Models & Databases (Relations, ER, Normalization) |
| 5 |
Data Architectures & Integration (ETL, ELT, Warehouse, Lake, Lakehouse, Cloud) |
| 6 |
Data Processing (BI, OLAP, Data Cleaning Paradigms) |
| 7 |
Midterm Week |
| 8 |
AI & Data Engineering Integration (Pipelines and Model Deployment) |
| 9 |
Introduction to Ethics |
| 10 |
Ethics in AI: XAI, FATE, Responsible AI |
| 11 |
Data Ethics: Privacy, Security, and Data Protection (GDPR, KVKK) |
| 12 |
Algorithmic Bias & Societal Impacts |
| 13 |
Responsible AI Development and Governance |
| 14 |
Case Studies & Final Discussions |
