Not logged in
Talk
Contributions
Create account
Log in
Navigation
Main page
About
People
Publications
Teaching
Resources
Research Blog
Wiki Functions
Recent changes
Help
Licensing
Page
Discussion
Edit
View history
Editing
Community Data Science Course (Spring 2015)
(section)
From CommunityData
Jump to:
navigation
,
search
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
== Assignments == The assignments in this class are designed to give you an opportunity to try your hand at using the technical skills that we're covering in the class. There will be no exams or quizzes. There will be weekly assignments that I will ask you to hand-in but will only be graded as ''complete/incomplete''. === Final Project Idea === :'''Maximum Length:''' 600 words (~2 pages double spaced) :'''Due Date:''' April 13 :'''Drop box:''' [[https://canvas.uw.edu/courses/963931/assignments/2816619 Turn in on Canvas]] In this assignment, you should concisely identify an community that you are interested in a source of data and/or and a list of at least 3-4 questions you might be interested in answering in the context of your final project. I am hoping that each of you will pick an area or domain that you are intellectually committed to and invested in (e.g., in your business or personal life). You will be successful if you describe the scope of the problem and explain why you are interested in using community data science methods. If you are unsure, asking a question about Wikipedia is probably among the safer paths. I will give you feedback on these write-ups and will let you each know if I think you have identified a questions that might be too ambitious, too trivial, too broad, too narrow, etc. === Final Project Proposal === :'''Maximum Length:''' 1500 words (~5 pages) :'''Due Date:''' May 4th (at 6pm) Building on your project idea assignment, you should describe the specific types of data you will collect, the steps you will take to collect the dataset, the limits and strength of these data for answering the question you have selected, and a description of the kinds of report and visualization you will make. And important step here is going to be ''framing'' your analysis. Why is this is an important question? Why do you care? What do we need to know (e.g., about the question, about underlying theories, about your business, about the topic, about the community) to understand this analysis? This will all need to be part of your final project and it's good opportunity to do this, or at least being doing it, here. I will give you feedback on these proposals and suggest changes or modifications that are more likely to make them successful or compelling and to work with you to make sure that you have the resources and support necessary to carry out your project successfully. === Final Project === :'''Presentation Date:''' June 2 :'''Paper Due Date:''' June 12 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 and community you have identified and information necessary to frame your question, (b) a description of the how you collected your data, (c) the results. 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 an 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. You to 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 and the dataset itself. Keep in mind that I will not be judging the quality or quantity of your code but rather the degree to which you have been successful at answering the ''substantive'' questions you have identified. ==== Presentation ==== Your presentation should do everything that your paper does and should provide me with a very clear idea of what to expect in your final paper. 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 too 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. ;Presentation Order: You '''must''' sign up for a presentation slot by editing [https://docs.google.com/spreadsheets/d/1P_saUgq1UEjg42KXRDXPa5pdaLTOKVUf2evEwmQWrUY/edit#gid=0 this spreadsheet]. If you are not on the sheet by Monday June 1st at 12:00pm, I will add you. ;Slides: You are encouraged to use slides for your talk but I will need your slides ahead of class. If you want to submit slides, you must upload slides in PDF format to [https://canvas.uw.edu/courses/963931/assignments/2816622 the assignment page in Canvas] by 12:00pm on Monday June 1st. I'm going to get everything in order on my laptop before class so we can make quick transitions. Because time will be very tight, if you do not submit slides, or if you submit them late, you will not be able to use slides for your talk. There will not be time in class for me to able to load your slides onto the computer. === Participation === The course relies heavily on participation. The material we're going to be covering is difficult and we're going to be covering it quickly. It is going to be extremely difficult to make up any missed classes. Attendance will be the most important part of participation and missing more than 1 class is going got make it extremely difficult to excel in our class. Nearly every week, we will begin by discussing challenges and problem sets that we'll define as a group at the end of the previous class. Please speak up and engage in this part of the class as well as asking questions anytime there is anything confusing. The "Participation Rubric" section of my [http://mako.cc/teaching/assessment.html detailed page on assessment] gives the rubric I tend to use in evaluating participation. === Grading === I have put together a very detailed page that describes [the grading rubric] we will be using in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below: * Participation: 30% * Final project idea: 5% * Final project proposal 10% * Final project presentation: 15% * Final paper: 40% === Weekly Coding Challenges === Each week I will give you all a set of weekly coding challenges before the end of class that will involve changing or adding to code that I've given you as part of the projects in the final parts of class to solve new problems. These coding challenges '''will not be turned in''' and '''will not be graded'''. I will share my solutions answers to each of the coding challenges by Monday morning of class in a Canvas discussion threads. As you will see over the course of the quarter, there are many possible solutions to many programming problems and my own approaches will often be different than yours. That's completely fine! Coding is a creative act! Please do not share answers to challenges before midnight on Sunday so that everybody has a chance to work through answers on their own. After midnight on Sunday, you are all welcome to share your solutions and/or to discuss different approaches. We will discuss the coding challenges for a short period of time at the beginning of each class.
Summary:
Please note that all contributions to CommunityData are considered to be released under the Attribution-Share Alike 3.0 Unported (see
CommunityData:Copyrights
for details). If you do not want your writing to be edited mercilessly and redistributed at will, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource.
Do not submit copyrighted work without permission!
To protect the wiki against automated edit spam, we kindly ask you to solve the following CAPTCHA:
Cancel
Editing help
(opens in new window)
Tools
What links here
Related changes
Special pages
Page information