This repository contains the instructional material for Jim Carlson's section of the 2019 Python Bootcamp at Ohio State University.
The exercises can be found on knode.io
and also in the problem_sets directory here. The Jupyter
notebooks for class are in weekly_lessons. I will also
be using material from Applying Python.
Data Science is a big subject, and we certainly can't learn it all in three weeks. But we can learn some of the theory and some of the tools, and apply these to interesting problems. This will give you an idea of the possibilities, of which there are many. I'm listing below some books and web sites which you may find helpful.
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Elegant SciPy, by Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashow, O'Reilly, pp 251. I partcularly like this one because it is short and treats very interesting problems.
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Python Data Science Handbook, by Jake VanderPlas, O'Reilly, pp 529.
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kaggle.com — data sources