- Anaconda3 with Jupyter and Spyder (or another editor)
- The python packages NumPy, Pandas, SciPy, Matplotlib, Seaborn & Scikit-learn
- Git
- Git, Anaconda, Jupyter, Spyder [Justin & Philipp]
- Introduction to Python [Justin]
- Types, Functions, Flow Control [Ilario]
- Vectors, Matrices, Dataframes (NumPy & Pandas) [Ilario & Alex]
- Plotting, Data Visualization, Exploration (Matplotlib, Seaborn & Bokeh) [Alex]
- Optimizing Parametric Functions, Integration, ODEs (SciPy & Scikit-learn) [Antoni]
- Best Practices, Testing, Performance [Philipp]
- Final Project
| September 13 | September 14 | |
|---|---|---|
| 09:30 - 11:00 | Block 1 | Block 5 |
| 11:00 - 11:30 | Coffee Break | |
| 11:30 - 13:00 | Block 2 | Block 6 |
| 13:00 - 14:00 | Lunch | |
| 14:00 - 15:30 | Block 3 | Block 7 |
| 15:30 - 16:00 | Coffee Break | |
| 16:00 - 17:30 | Block 4 | Block 8 |
- Learn X in Y minutes where X = Python
- The Python Tutorial
- 10 Minutes to Pandas
- Scipy Lecture Notes
- Python Data Science Handbook
- Pythonic Preambulations
- Subtleties of Color
- A Beginner’s Guide to Optimizing Pandas Code for Speed
- Raymond Hettinger: Beyond PEP 8 -- Best practices for beautiful intelligible code
- Trey Causey: Testing for Data Scientists
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