- Introduction to Numpy
- Introdution to Pandas
- Basic Data Visualisation
- Machine Learning with Scikit-Learn
- Numpy Challenges
- Scipy Sparse Challenges
- Pandas Challenges
- [Machine Learning Case Study & Challenges](./sklearn/case study)
Differently from other sections, Data Science Challenges will be provided as Jupyter Notebooks. If you don't know what a Jupyter notebook is, or how to use it, please take a look at this quick introductory tour: IPython Notebook Beginner Guide.
For additional details and materials on Jupyter and IPython, I suggest this Jupyter Notebook the Definitive Guide:
If you want an introductory overview of Python for Data Science, I strongly recommend
Scipy Lecture Notes: a community driven project where you can find
tutorials (for non-experts) on the scientific Python ecosystems.
Additional Books for further readings:
- Scipy and Numpy
- Python Data Science Handbook
- Elegan Scipy
- Python for Data Analysis
- Introduction to Machine Learning with Python
- Building Machine Learning Systems with Python
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Materials in the
numpy
section is adapted and updated from https://github.com/leriomaggio/numpy_euroscipy2015 © 2015-2018 Valerio Maggio. -
The materials in the
pandas
section has been mostly taken and adapted from https://github.com/alanderex/pandas-pydata-2017 © 2015-2017 Alexander C.S. Hendorf, Königsweg GmbH, Mannheim- The Pandas anti-patterns section has been adapted from the fantastic tutorial @ PyCon Otto by Pietro Battiston Pandas, the wrong way source: https://pietrobattiston.it/python:pycon
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The materials in the matplotlib section has been mostly taken and adapted from https://github.com/matplotlib/AnatomyOfMatplotlib