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- 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):
- PyCharm Community edition is free and free licences for students available
- Sublime Text This is an editor, not an IDE, but it has many features for programmers
- Atom Same as Sublime, this is an editor
- Emacs and Vim with plugins These editors have quite steep learning curve
- 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.,
- 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
- Week 12 (November 18 -- November 22)
- Computer Vision and OpenCV Lab
- Week 13 (November 25 -- November 29)