Well it has taken a week to get to the first rough draft of my new SI502 text book titled, “Python for Informatics”. Thanks to Allan B. Downey and Cambridge University Press and their forward looking open copyright, I already have 162 pages done. So far I have written a preface, changed Chapter 1, changed the order of the chapters, removed all text regarding recursion, removed object oriented and graphics content that was too advanced, and ran it all through LaTeX.
Here is a link to the most recent draft: Python for Informatics: Exploring Information. (This will always point to the latest draft.)
Here is the text from the preface of the book:
Remixing an Open Book
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 “remixing” 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 lifelong data handling skills using Python. Few of my students were planning to be professional computer programmers. Instead, they planned to 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.
Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data-oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all.
In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, using web services, parsing XML and JSON data, and creating and using databases using Structured Query Language.
The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer.
Students who find this book interesting and want to further explore should look at Allen B. Downey’s Think Python book. Because there is a lot of overlap between the two books, students will quickly pick up skills in the additional areas of technical programming and algorithmic thinking that are covered in Think Python. And given that the books have a similar writing style, they should be able to move quickly through Think Python with a minimum of effort.
As the copyright holder of Think Python, Allen has given me permission to change the book’s license on the material from his book that remains in this book from the GNU Free Documentation License to the more recent Creative Commons Attribution — Share Alike license. This follows a general shift in open documentation licenses moving from the GFDL to the CC-BY-SA (e.g., Wikipedia). Using the CC-BY-SA license maintains the book’s strong copyleft tradition while making it even more straightforward for new authors to reuse this material as they see fit.
I feel that this book serves an example of why open materials are so important to the future of education, and want to thank Allen B. Downey and Cambridge University Press for their forward-looking decision to make the book available under an open copyright. I hope they are pleased with the results of my efforts and I hope that you the reader are pleased with our collective efforts.
I would like to thank Allen B. Downey and Lauren Cowles for their help, patience, and guidance in dealing with and resolving the copyright issues around this book.
Ann Arbor, MI, USA
September 9, 2013