The Python ecosystem for data science

1. Scipy Lecture Notes: http://www.scipy-lectures.org/

2. Python scientific computing ecosystem: http://www.scipy-lectures.org/intro/intro.html

3. Fully-featured Scientific Python distributions:

a. Anaconda: https://www.anaconda.com/download/

b. EPD: https://store.enthought.com/downloads/

c. WinPython: https://winpython.github.io/

4. IDE for Python: Spyder https://pythonhosted.org/spyder/

5. Numpy: numerical computing with powerful numerical arrays objects, and routines to manipulate them. http://www.numpy.org/

Reference Guide: https://docs.scipy.org/doc/numpy/reference/index.html

6. Scipy: high-level numerical routines. Optimization, regression, interpolation, etc http://www.scipy.org/

Reference Guide: https://docs.scipy.org/doc/scipy/reference/

7. Matplotlib, Python 2D plotting library: https://matplotlib.org/

Pyplot: https://matplotlib.org/api/pyplot_summary.html

8. Pandas, Python Data Analysis Library (data input-output, basic statistics and graphics): http://pandas.pydata.org/

9. Seaborn: statistical data visualization library based on matplotlib: http://seaborn.pydata.org/

10. Scikit-learn, machine Learning in Python: http://scikit-learn.org/stable/

Manual: http://scikit-learn.org/stable/user_guide.html

Manual in pdf: http://scikit-learn.org/0.20/_downloads/scikit-learn-docs.pdf

Tutorial: http://www.scipy-lectures.org/packages/scikit-learn/index.html#scikit-learn-chapter