Data Science Course IFT6758

Lab Materials

Week 1 (September 2 – September 8):
- Data Science Class: Lab Introduction, slides
Week 2 (September 9 – September 15):
- Introduction to Python, notebook, solution, notebook download, solution download
- More resources about Python:
  - Beginner’s guide to Python
  - The Zen of Python
  - Style guide for Python code (PEP8)
  - IDE’s (Interactive development environments):
1. PyCharm Community edition is free and free licences for students available
2. Sublime Text This is an editor, not an IDE, but it has many features for programmers
3. Atom Same as Sublime, this is an editor
4. Emacs and Vim with plugins These editors have quite steep learning curve
5. Spyder
Week 3 (September 16 – September 22)
- Introduction to NumPy, SciPy and Matplotlib, notebook
Week 4 (September 23 – September 29)
- Computing resources and setup for the project. Miniconda , Putty
- The simplest way to make use of a library on the server is to first load the corresponding module, e.g., module load miniconda/3
Week 5 (September 30 – October 6)
- Introduction to Pandas, notebook
Week 6 (October 7 – October 11)
- Practical Data Science with Scikit-Learn
Week 7 (October 14 – October 18)
- Machine learning workflow and git workflow.
Week 8 (October 21 – October 25)
- Reading week
Week 9 (October 30 – November 3)
- Mid-term exam
Week 10 (November 4 – November 8)
- Mid-term project presentation
Week 11 (November 11 – November 15)
- Natural Language Processing with NLTK
  - NLTK tutorial
Week 12 (November 18 – November 22)
- Computer Vision and OpenCV Lab
  - Slides
  - Live Demo Code & Files
Week 13 (November 25 – November 29)