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

=== Final Project ===
:'''Presentation Date:''' June 1, 2016
:'''Paper Due Date:''' June 10, 2016 at midnight.

''' [https://canvas.uw.edu/courses/1039305/assignments/3293283 Hand in your presentation here] '''

''' [https://canvas.uw.edu/courses/1039305/assignments/3293284 Hand in your final paper here] '''

For your final project, I expect you to build on the first two assignments to describe what they have done and what you have found. I'll expect every student to give both:

# A short presentation to the class (10 minutes)
# A final report that is not more than 4500 words (~18 pages)

I expect that your reports will include text from the first two assignments and reflect comprehensive documentation of your project. Each project should include: (a) the description of the question you have identified and information necessary to frame your question, (b) a description of the how you collected your data, (c) the results, (d) a description of the scope or limitations of your conclusion.

A successful project will tell a compelling story and will engage with, and improve upon, the course material to teach an audience that includes me, your classmates, and Comm Lead students taking this class in future years how to take advantage of community data science more effectively. The very best papers will give us all a new understanding of some aspect of course material and change the way I teach some portion of this course in the future.

==== Paper and Code ====

Your final project should include detailed information on:

* The problem or area you have identified and enough background to understand the rest of your work and its importance or relevance.
* Your research question(s) and/or hypotheses.
* The methods, data, and approach that you used to collect the data plus information on why you think this was appropriate way to approach your question(s).
* The results and findings including numbers, tables, graphics, and figures.
* A discussion of limitations for your work and how you might improve them.

If you want inspiration for how people use data science to communicate this kinds of findings broadly and effectively, take a look at great sources of data journalism including [http://fivethirtyeight.com/ Five Thirty Eight] or [http://www.nytimes.com/upshot/ The Upshot at the New York Times]. Both of these publish a large amount of excellent examples of data analysis aimed at broader non-technical audiences like the ones you'll be communicating with and quite a bit of their work is actually done using Python and web APIs!  A simple Five Thirty Eight story will include a clear question, a brief overview of the data sources and method, a figure or two plus several paragraphs walking through the results, followed by a nice conclusion. I'm asking you to try to produce something roughly like this.

Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 4,5000 words to show me what you've learned. As a result, you should do ''more'' than FiveThirtyEight does in a single story. You can ask and answer more questions, you can provide more background, context, and justification, you can provide more details on your methods and data sources, you can show us more graphs, you can discuss the implications of your findings more. Use the space I've given you to show off what you've done and what you've learned!

Finally, you should also share with me the full Python source code you used to collect the data as well as the dataset itself. Your code along will not form a large portion of your final grade. Rather, I will focus on the degree to which you have been successful at answering the ''substantive'' questions you have identified.

At least 25% of your grade for this project will be determined by the visualizations and tables in your report. Good visualizations should "stand alone" and motivate the core results in your paper all by themselves. A good question to keep in mind is "could I tell this story with the visualizations and a tweet?"

==== Presentation ====

Your presentation should provide me with a very clear idea of what to expect in your final paper. However, don't treat it as a comprehensive overview of your paper: I would rather you tell a subset of the story well than the whole story in a rushed fashion. I'm going to give you all at least a paragraph of feedback after your talk. This will be an opportunity for me to see a preview of your paper and give you a sense for what I think you can improve. It's to your advantage to both give a compelling talk and to give me a sense for your project.

;Timing: All presentations will need to be '''a maximum of 7 minutes long'' with additional 2-3 minutes for questions and answers. Timing is going to be tight and I'm going to set an alarm and stop presentations that go too long. Concisely communicating an idea in the time allotted is an important skill in it's own right.


;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class. See link at top of this section.