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Python for Informatics

Python for Informatics

Python for Informatics

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Book Details:

Pages:244 pages
Size:1.37 MB
License:Pending review


It is quite natural for academics who are continuously told to "publish or perish" to want to always create something from scratch that is their own fresh creation. This book is an experiment in not starting from scratch, but instead "re-mixing" the book titled Think Python: How to Think Like a Computer Scientist written by Allen B. Downey, Jeff Elkner and others.

In December of 2009, I was preparing to teach SI502 - Networked Programming at the University of Michigan for the fifth semester in a row and decided it was time to write a Python textbook that focused on exploring data instead of understanding algorithms and abstractions. My goal in SI502 is to teach people life-long data handling skills using Python. Few of my students were planning to be be professional computer programmers. Instead, they planned be librarians, managers, lawyers, biologists, economists, etc. who happened to want to skillfully use technology in their chosen field.

I never seemed to find the perfect data-oriented Python book for my course so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.

The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning.



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