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For a newbie in data science , it is very important to divide your learning process into modules. Modularity makes the learning process easy by studying step by step approach and learning one module at a time.Here we will go from basic to master step wise.

In order to build efficient data science models , we need to take up a base programming language .We have multiple languages that can be used for the same e.g. python, R ,S etc.Then why Python? We use python as our base language because it is open sourced which means it is available free of cost , plus it provides us various packages and libraries in order to make a flexible and efficient machine learning model.These two python files have novice level code i.e. even if you are a non programmer you will be able to pickup the basic python which is necessary for using python in our data science branch,

1. Introduction to python - It is basically gives you base and is the basic level python relevant to data science.
2. Numpy -NumPy is the fundamental package for scientific computing with Python. It contains among other things:

a powerful N-dimensional array object

sophisticated (broadcasting) functions

tools for integrating C/C++ and Fortran code

useful linear algebra, Fourier transform, and random number capabilities