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 2023)
(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!
=== Final Project === 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 (8 minutes max) # 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, defensible story in prose and plots and will contain source code sufficient to reproduce the results. ==== Final Presentation ==== ;'''Presentation Date:''' * Presentations need to be shared in the Canvas discussion by '''Monday May 29 at 11:59pm''' * Feedback on others presentations need to be posted by '''Thursday June 1 at 11:59pm''' ;'''Turning in:''' * Upload your video as a new thread in this [https://canvas.uw.edu/courses/1633288/discussion_topics/8134432 Canvas discussion] ;'''Watching presentations and feedback:''' * We're expecting ''every'' person in the class to watch ''every'' presentation. It should take about 2 hours 30 minutes. * Leave substantive feedback (see 5-question list below as a guide to feedback) by replying to the threads of the people in [https://teams.microsoft.com/l/message/19:a289c7b44b704149a725047dcafc814d@thread.tacv2/1684191632538?tenantId=f6b6dd5b-f02f-441a-99a0-162ac5060bd2&groupId=189ea783-b3e2-4b4e-83ce-74ebc15db6a5&parentMessageId=1684191632538&teamName=Community%20Data%20Science%20(Spring%202023)&channelName=Final%20Projects&createdTime=1684191632538&allowXTenantAccess=false your final presentation group] [requires Teams access]. We're expecting that you should spend about 15 minutes writing up meaningful feedback for these people. This should take about 90 minutes. * Leave short feedback (spending 1 minute or so is fine) for everybody who is ''not'' in your presentation group. Saying you love their work is great, but the truest expression of love in data science is intellectual engagement with someone's hard work. Ask a question, make a connection to other ideas and work, or point out a place to dig deeper. ;Length: All presentations will need to be '''a maximum of 8 minutes long'''. Do not exceed 8 minutes and 0 seconds and don't edit your video to speed it up if you go too long. Concisely communicating an idea in the time allotted is an important skill in its own right! ;Slides: You are encouraged to use slides for your talk. Please keep in mind that your slides are meant to be additive, not a teleprompter. ;Recording your talk: There are a range of options for recording your talk. If all else fails, you can join a Zoom room by yourself, share your screen, and record the sessions. It's been working for the class all quarter long so it'll probably work for you too! Your presentation should provide the teaching team and your classmates 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. For instance, you can give a completely successful presentation by describing the motivation and walking through one plot in your paper. The teaching team will provide you with substantial feedback on your presentation. 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. Here is a list of questions to think about both when providing in your feedback and when structuring your talk: # In your own words, what research question did you hear the presenter answer? What are they trying to communicate? How well did they answer their question or achieve their goals? Did you find the answer compelling? Why or why not? # Be critical. What problems, challenges, or limitations do you see with the way that the presenter is trying to measure things? What are the risks or threats to the validity of the work? # How would you improve the work? What additional sort of data do you think could be helpful? What sort of additional analysis or visualization would be helpful? What additional interpretation would be helpful? # If you saw this as a YouTube video or retweeted on Twitter, what sort of questions would you leave in the comments? # If you were doing this project and presenting it to a supervisor, how would they respond? What changes would you want to make to address this audience a result? What if you were presenting it a colleague, collaborator or friend? ==== Final Paper (and Code!) ==== :'''Due Date:''' Thursday June 8 at 11:59 p.m. :'''Turning in:''' Turn in a PDF of your paper to [https://canvas.uw.edu/courses/1633288/assignments/8231894 the Canvas dropbox]; Upload all your code into the [https://github.com/kayleachampion/spr23_CDSW/tree/main/final_project_code final_project_code directory in the class Github repository]. 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 similar. Keep in mind that most stories on Five Thirty Eight are under 1000 words and I'm giving up to 4,500 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 data set itself. Your code alone 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. Visualization is critical to storytelling, so 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?"
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