Weekly Materials
A crash course on Programming in Python
Professor's lecture slides (PDF)
A brief introduction to GitHub (quick tour; self-study)
Professor's lecture slides (PDF)
Finger exercises (at the end of the lecture; self-study)
Hands-on tutorials and practice exercises
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Exercise 1
Distribution of Exercise sheet 1
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References & Resources
GitHub Quick Tour
Brief introduction to GitHub
Additional Notes
Week 3: Get up to speed - week 2
Learning Objectives
- Master Python programming fundamentals for data analysis
- Work effectively with Jupyter notebooks for interactive computing
- Understand NumPy and SciPy for scientific computing
- Set up GitHub for version control and collaboration
- Build foundation skills for machine learning implementations
Topics Covered
- Python Crash Course: Essential Python concepts for data science
- Jupyter Notebooks: Interactive development environment
- GitHub Introduction: Version control basics (self-study)
- NumPy Fundamentals: Arrays, linear algebra operations (self-study)
- SciPy Introduction: Scientific computing tools (self-study)
- Practical Exercises: Hands-on finger exercises
Schedule
- Lecture: Monday, September 29, 2025 (10:15 - 12:00)
- Practice Session: Monday, September 29, 2025 (16:30 - 18:00)
- TA Session: Monday, September 29, 2025 (17:15 - 18:00)
Key Skills Developed
- Python data structures and control flow
- Jupyter notebook best practices
- Basic array operations with NumPy
- Scientific computing with SciPy
- Version control workflow
Assignments
- Exercise 1: Distributed this week
- Complete finger exercises from lecture
- Set up GitHub repository for course work