Editing PyData Seattle 2015 proposal

;Title:

;Short Description (400 char):

The Community Data Science Workshops are the 
- new curriculum developed in 2014-2015
- taught three times in seattle
- focused on absolute beginners
- nearly 400 people have sign up for the first three sessions
- and 200 were admitted and attended one of the workshop series
- NNN volunteer mentors
- curriculum has been adapted and run elsewhere with more in progress

;Abstract:

The Community Data Science Workshops are a series of project-based workshops for anyone interested in learning how to use programming and data science tools to ask and answer questions about online communities like Wikipedia, Twitter, free and open source software, and civic media.

The workshops are for people with no previous programming experience. The goal is to bring together both researchers and academics as well as participants and leaders in online communities. The workshops have all been free of charge and are open to the public.

The sessions are schedule for one Friday evening and three Saturdays all day. Each session involves a period for lecture and technical demonstrations in the morning. This is followed by a lunch. The rest of the day consists of self-directed work on programming and data science projects supported by more experienced mentors.

Our goal is that, after the three workshops, participants will be able to use data to produce numbers, hypothesis tests, tables, and graphical visualizations to answer questions like:

- Are new contributors to an article in Wikipedia sticking around longer or contributing more than people who joined last year?
- Who are the most active or influential users of a particular Twitter hashtag?
- Are people who participated in a Wikipedia outreach event staying involved? How do they compare to people that joined the project outside of the event?

Our very first workshops was originally modeled after the Boston Python Workshop but most our curriculum is brand new and has been developed and modified by the mentors and with feedback from the participants.

features:

- teaching to complete novices
- focusing on using data from day1 (e.g., not like BPW)
- focusing on data best practices along the way

techical details:
- moved to anaconda as default (reduced number of)
- moved to python 3 in 2015 with zero hitches (solved /many/ encoding issues)