Python is one of the most popular programming languages for data science and therefore enjoys a large number of useful add-on libraries developed by its great community. Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower layer Fortran and C implementations for fast and vectorized operations on multidimensional arrays.
Here we will start our journey by learning the following libraries:
Install Anaconda from:
https://www.continuum.io/downloads
Install PyCharm:
https://www.jetbrains.com/pycharm/download/
Setup in PyCharm:
https://www.youtube.com/watch?v=9kPe8zWefoI
We will use the following book to learn:
Learning pandas - Python Data Discovery and Analysis Made Easy
https://www.amazon.com/Learning-pandas-Python-Discovery-Analysis-ebook/dp/B00W9Q7VPA
Python for Data Analysis
http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793/