Editing Community Data Science Course (Spring 2016) (section)

== Readings ==
This class is going to be a studio and project based class. Although we will not rely very heavily on readings or discussing them in depth in class, I'm strongly recommending a book that will cover the material we go over in class and which will provide a reference work for you to refer to:

# '''[http://www.pythonlearn.com/book.php Python for Informatics: Exploring Information]''' by Charles Severance. The book is available online for free but you can also buy a physical copy of the book [http://www.amazon.com/gp/product/1492339245/ref=as_li_ss_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=1492339245&linkCode=as2&tag=drchu02-20 from Amazon] or get an electronic copy from the [http://www.amazon.com/dp/B00K0O8HFQ Kindle Store]. According to the book's website: "The goal of this book is to provide an Informatics-oriented introduction to programming. The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve data analysis problems common in the world of Informatics."

There will still be a few readings throughout the semester including at least the following:

# Chapter 2 ("How to Keep Score") of [http://www.amazon.com/Lean-Analytics-Better-Startup-Faster/dp/1449335675 Lean Analytics] by Alistair Croll and Benjamin Yoskovitz.
# Chapters 1 and 2 of [http://masteringmetrics.com/ Mastering Metrics] by Joshua D. Angrist and Jorn-Steffen Pischke. 
# Several excellent uses of data in articles from [http://www.fivethirtyeight.com FiveThirtyEight] and similar sources. 

If you run across a particularly great example of a story told with data, please pass it along!

For book chapters, I'll make pdfs available at least 1 week ahead of time. In general, you should expect to spend an hour or less reading per week and 6 or more hours a week on programming tasks.