Data Warehouse 1 Course Syllabus
This course introduces the fundamentals of data warehousing, covering key concepts, architectures, and practical implementations using open-source tools. The course is designed for master’s students with diverse backgrounds and prepares them for an advanced Data Warehouse course.
Course Structure
- 7 weeks of classes (3 hours each)
- 1 final exam session (3 hours)
- Combination of lectures, in-class exercises, quizzes, and computer labs (starting from Week 3)
Weekly Schedule
Week 1: Introduction to DBMS and Data Warehouses
- Challenges for DBs
- Brief history of DBs
- Founding principles of DBMS
- Data Warehouse motivations and definitions
- OLTP vs OLAP
- DW industrial landscape
- In-class exercises: Identifying OLTP vs OLAP scenarios
Materials:
Week 2: Data Warehouse Concepts and Relational Schemas
- Multi-dimensional model
- DW architecture and components
- Data model: facts, measures, dimensions, cube
- Multi-dimensional queries: OLAP operations
- Relational schemas for DWHs
- Dimensional modeling
- Fact tables and dimension hierarchies
Materials:
Week 3: SQL 101
- Quiz on Week 1 and Week 2 material (first 30 minutes)
- SQL review with focus on DW-specific queries
- Advanced SQL techniques for data warehousing
Materials:
Week 4: SQL for Data Warehouses
- Avanced SQL course
- Lab: Advanced SQL
Materials:
Week 5: SQL for Data Warehouses (continued)
Week 6: TBA
Week 7: Data Manipulation with Python
- Quiz on Week 3 to Week 6 material (first 30 minutes)
- Graded Lab
Week 8: Final Exam
- Comprehensive exam covering all course topics (3 hours)
Assessment
- In-class exercises and participation (20%)
- Graded quizzes (2 or 3 quizzes, total 30%)
- Lab reports (20%)
- Final exam (30%)
Required Tools (Open-source and Free)
- PostgreSQL for relational database examples
- Python with NumPy and Pandas (a recent version)
- MongoDB for NoSQL examples
- Additional open-source tools to be announced for specific labs