Editing Community Data Science Course (Spring 2023) (section)

== 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. Like many technical subjects, Data Science tends to build on earlier ideas, so I strongly suggest that you devote substantial time to assignments every single week.

=== Weekly Coding Challenges ===

:'''Due Date:''' Sundays at 11:59 p.m. on most weeks.
:'''Turning in:''' Upload your <code>.ipynb</code> files to [https://github.com/kayleachampion/spr23_CDSW the class Github repository] for the specific week

Most weeks 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.  We expect you to make substantial progress on your weekly assignment each week and we expect that many or most of you will have (mostly) working solutions or at least some code code for every single problem. These coding challenges will be turned in but will not be graded on effort, not on full correctness. They will be graded as ''complete/incomplete''.

I will share my solutions to each of the coding challenges via email. 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 on the night before class so that everybody has a chance to work through answers on their own. After midnight on Sunday, you are all welcome and encouraged to share your solutions and/or to discuss different approaches.  We will discuss the coding challenges at the beginning of each class.

Our plan is to ''randomly'' select folks each day of class and ask you to share your answer to one or more specific problems with the rest of the class. Everybody in the class will be "in the mix" for being called upon every time we select a person and we may call you more than once in a class. When you are called, we will pull up the code you wrote for your homework on the projector and ask you to walk us through and explain your choices in your work on the program challenges.

=== Final Project Idea ===

:'''Maximum Length:''' 600 words (~2 pages double spaced)
:'''Due Date:''' Week 3 (April 10)
:'''Turning in:''' Turn in PDF in to [https://canvas.uw.edu/courses/1633288/assignments/8231677 the Canvas dropbox]

In this assignment, you should identify an area of interest, at least one sources of relevant data, and at least 3-4 questions that you plan to explore. We will discuss appropriate data sources for your project in the first and second week of the course. I am hoping that each of you will pick an area 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 think the data sources you've identified are relevant. 
 
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. In week 2, we will walk through successful projects from previous course offerings to give you an idea of the correct scope.

=== Final Project Proposal === 

:'''Maximum Length:''' 1500 words (~5 pages)
:'''Due Date:''' Week 8 (May 15)
:'''Turning in:''' Turn in PDF [https://canvas.uw.edu/courses/1633288/assignments/8231678 the Canvas dropbox]

This proposal should focus on two questions:

* Why are you planning to do this analysis? Make sure to introduce any background information about the topic, the community, your business, or anything else that will be required to properly contextualize your study.
* What is your plan? Describe the data sources will you collect and how they will be collected. Are there any blind spots given the data you have available? Are there any visualizations or tables that you plan to build?

Your proposal should frame your final analysis, but it's also a chance to "sanity check" your plan. I will give you feedback on these proposals and suggest changes or modifications that are more likely to make them successful or compelling. I will also work with you to make sure that you have the resources and support necessary to carry out your project successfully.

Be as specific as possible about the data available on the sources you've chosen. I expect that you will have written at least some of the final code that you will use in this course. At the very least, you will have identify the documentation and the API endpoints that are required. If there are libraries that you think may help with access, note them.

Unless you've talked to us, we expect every person to include at least one visualization in their final project. For the proposal, please include "dummy" versions of these that shows us what the x and y axes are. It's OK (and maybe best!) if these are simple and hand drawn!

=== 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?"

=== 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 to 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. If you are feel confused about a new Python concept, it's highly unlikely that you are the only one. If there is anything I can do to help you participate in class, please let me know in the anonymous feedback.