Intro to Python

Introduction to the use of Python for data science

Why Python

Python has a syntax that is efficient, somewhat tries to resemble natural language, which allows for ease of use. (when compared to other languages)
With it’s popularity, many other popular scientific software libraries were implemented:
- Numpy - To support numeric analysis as naturally as Matlab does
- Matplotlib - Similar plotting functionality to Matlab
- Pandas - Data frame and associated manipulations (similar to R)
- sckikit-learn - Machine Learning algorithms (similar to caret in R)
- IPython/Jupyter - Notebook concept (similar to Mathematica/Sage)
There are better tools for specific use cases, but for general purpose programming, Python is preferred.
Large community, many resources!

Writing a workflow as a program allows for pushing the compute time to the cloud.
To scale (distribute) your workflow across a very large dataset.
Python and third party libraries are open source, no expensive enterprise licensing.