Community Data Science Course (Spring 2023)
- Community Data Science: Programming, Web Data Collection, and Data Science
- COM597 A / COMMLD 570 B — Offered jointly between the University of Washington Department of Communication MA/Program / and the Communication Leadership
- Location: Communications Building (CMU) Room 104
- Instructors: Benjamin Mako Hill / email@example.com and Kaylea Champion firstname.lastname@example.org
- Course Website: We will use Canvas for assignments and [To Be Decided]. Everything else will be linked on this page.
- Course Catalog Description (from Communication Leadership):
- This course will introduce basic programming and data science tools to give students the skills to use data to read, critique, and produce stories and insights. The class will cover the basics of the Python programming language, acquiring and processing public data, and basic tools and techniques for data analysis and visualization. We will focus on gaining access to data and basic data manipulation rather than complex statistical methods. The class will be built around student-designed independent projects and is targeted at students with no previous programming experience.
Overview and Learning Objectives
In a world that is increasingly driven by software and data, developing a basic level of fluency with programming and the basic tools of data analysis is a crucial skill. This course will introduce basic programming and data science tools to give students the skills to operate in a data-driven environment.
In particular, the class will cover the basics of the Python programming language, an introduction to web APIs, and will teach basic tools and techniques for data analysis and visualization. In order to efficiently cover an end to end data analysis project, we will focus on a series of publicly available data sets. Time will also be reserved to cover data access for sevearl popular social media platforms.
As part of the class, participants will learn to write software in Python to collect data from web APIs and process that data to produce numbers, hypothesis tests, tables, and graphical visualizations that answer real questions. The class will be built around student-designed independent projects. Every student will pick a question or issue they are interested in pursuing in the first week and will work with the instructor to build from that question toward a completed analysis of data that the student has collected using software they have written.
This is not a computer science class and I am not going to be training you to become professional programmers. This introduction to programming is intentionally quick and dirty and is focused on what you need to get things done. We will focus on effectively answering questions from public data sets by writing your own software and by managing and communicating more effectively with programmers.
I will consider this class a complete success if, at the end, every student can:
- Write or modify a program to collect a dataset from a publicly available data source.
- Read web API documentation and write Python software to parse and understand a new and unfamiliar web API.
- Use both Python-based tools as well as other tools like LibreOffice, Google Docs, or Microsoft Excel to effectively graph and analyze data.
- Use web-based data to effective answer a substantively interesting question and to present this data effectively in the context of both a formal presentation and a written report.
- The ideal outcome is that students will have the working knowledge to more effectively collaborate with data professionals in their careers. They will be both more informed about the process and more likely to spot undeclared assumptions in their colleague's work.
Note About This Syllabus
You should expect this syllabus to be a dynamic document and you will notice that there are a few places marked "[To Be Decided]." Although the core expectations for this class are fixed, the details of readings and assignments will shift based on how the class goes. As a result, there are three important things to keep in mind:
- Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a [To Be Decided] one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first.
- Closely monitor your email. Because this a wiki, you will be able to track every change by clicking the History button on this page. I will also summarize these changes in an announcement that will be emailed to everybody in the class.
- The teaching team will ask the class for voluntary anonymous feedback frequently—especially toward the beginning of the quarter. Please let me know what is working and what can be improved.
This class is going to be a studio and project based class. Although we will not rely very heavily on readings or discussing them in depth in class, I'm strongly recommending a book that will cover the material we go over in class and which will provide a reference work for you to refer to:
- Python for Everyone. The book is available online for free and will provide a handy reference for the Python language.
I will list required chapters in the weekly notes below. In general, you should expect to spend an hour or less reading per week and 6-10 hours a week on programming tasks. Many weeks it will be more.
Much like Math or other technical courses, this course will build on itself every week. You should make every effort to cover the reading and exercise material every week in preparation for the next week.
- I expect you to come to class every day with your own laptop. Windows, Mac OS and Linux are all fine but an iPad or Android tablet is not going to cut it. We're going to install software during the class and you'll be working on projects for homework so please bring the same laptop each time. If for some reason your laptop dies mid-course, please contact me so we can get your new one up to speed.
- If you need access to a computer, please reach out to me as soon as possible. The Department has laptops you can borrow for the course, but it's important to have that laptop in the first week.
Staying in Touch [To Be Decided]
The teaching team is still working out details on how we're going to stay in touch outside of class and what the best ways to reach us will be. We are committed to building some sort of chat system. The most likely situation is that we'll use a Discord server for this purpose.
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
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. These coding challenges will be turned in but will not be graded on effort not 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 [To Be Decided] so that everybody has a chance to work through answers on their own. After midnight on [To Be Decided], you are all welcome and encouraged 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.
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 (details/link [To Be Decided])
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 (details/link [To Be Decided])
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. Identify the documentation and the API endpoints where required. If there are libraries that you think may help with access, note them.
- Presentation Date: Last week of the quarter (date/details/link [To Be Decided])
- Paper Due Date: End of finals week (date/details/link [To Be Decided])
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, defensible story in prose and plots and will contain source code sufficient to reproduce the results.
Final 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 Five Thirty Eight or 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 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.
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?"
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. 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.
Many details of the presentation are still [To Be Decided].
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.
In general, my teaching style is more conversational than a formal lecture. I prefer that students feel they can "politely interrupt" at any time to seek clarification or make a well-informed point.
Assignments will accrue to your final grade in the following way:
- 10% will be class participation including attendance, participation in discussions, and group work
- 30% significant effort towards weekly assignments
- 5% will be the Final Project Idea
- 10% will be the Final Project Proposal
- 10% will be your Final Presentation including your slides and presentation.
- 35% will be the Final Project write up including visualizations
|This section will be modified throughout the course to introduce the week's material and any hand-ins.|
Week 1: March 27 [Tentative]
Today we'll be getting software installed and getting setup with Python.
- [To Be Decided]
Class Schedule: [Tentative]
- Class overview and expectations — We'll walk through this syllabus.
- Day 1 Exercise — You'll install software including the Python programming language and run through a series of exercises.
- Day 1 Tutorial — You'll work through a self-guided tutorial introducing you to some basic concepts. When you're done, you'll meet with me and I'll check you off. [Tentative]
- A few interesting links we discussed in class are here [Tentative]
- For exercise 5, look at chapter 3 of the textbook. This introduces "if" statements.
By the end of class you will:
- Have a working python environment on your personal laptop.
- Have written your first program in the python language.
Week 2: April 3 [Tentative]
Today we'll be doing a crash course is basic programming in Python.
Assignment Due (nothing to turn in): [Tentative]
- Read chapters 2 and 3 of Python for Everyone:
- Chapter 2, Variables
- Chapter 3, Conditionals
- Finish setup, tutorial and code academy in the week 01 exercises. [Tentative]
- Do the Tip Calculator exercise in Code Academy. You can access this exercise after you finish the first 14 exercises. [Tentative]
Class schedule: [Tentative]
- Discuss a successful final project from a previous version of the class. [Tentative]
- Lecture notes [To Be Decided]
- Review material from last week: variables, assignments, if statements
- Introduce new material: loops and lists
- Project time — We'll begin working on the wordplay independent projects independently or in small groups.
- Introduce /Day 2 coding challenges
By the end of class you will:
- Have written a program with loops and lists.
- Have a better understanding of the expectations for your final project, and be ready to hand in your initial assignment.
Week 3: April 10 [Tentative]
Today we'll be doing introducing some additional programming concepts in Python including aggregating and counting with dictionaries.
Assignment Due: [Tentative]
- Final project idea (Canvas link [Forthcoming])
- Finish Wordplay examples
- Read chapter 4, 5 of Python for Informatics: [Tentative]
- Functions (this is mostly new)
- Iteration (this is mostly review)
- Dictionaries [To Be Decided]
Class schedule: [Tentative]
- Go over last week's assignment
- Dictionaries and aggregations see the /Day 3 notes
- A break
- Discuss average, median using the Wordplay data.
- Project time—We'll begin working on a series of project based on the Baby names project.
- Introduce the /Day 3 coding challenges
- Python_data_types_cheat_sheet A cheat sheet with everything we've covered in class so far including today.
Week 4: April 17 [Tentative]
Today we'll be using Python to read and write files from disk and be learning to do some basic tricks with a very useful Python module called Pandas.
Assignment Due: [Tentative]
- Day 3 coding challenges
- Files, and Basic Pandas (read_csv, group_by) [Tentative]
- Let's discuss two visualizations I found. [Tentative]
- Go over last week's assignment.
- Discuss histograms in Python, and build a few.
- Project time—We'll reuse the babynames code.
- /Day 4 coding challenges [To Be Decided]
Week 5: April 24 [Tentative]
Today we'll be learning about the basic of web APIs and JSON.
Turn in (on canvas!) solution to this problem:
- Finish Baby Names week #2 coding challenges
- Go over last week's assignment and review histograms.
- Discuss APIs and downloading data from the internet. Refer to /Day 5 notes
- Spend time on /Day 5 coding challenges
Week 6: May 1 [Tentative]
Today we'll be putting everything together and walking through a project that builds a dataset from the Wikipedia API from start-to-finish.
- Let's discuss remaining schedule
- Discuss data downloading and cleaning. Refer to /Day 7 notes
- We will be discussing this data set: https://data.seattle.gov/Transportation/Collisions/vac5-r8kk
- Introduce and start working on /Day 7 coding challenges
Week 7: May 8 [Tentative]
Today we'll be introducing two additional web APIs (still [To Be Decided]) but we're considering Yelp, Reddit, and Twitter.
- Final Project Proposal (Canvas link is [Forthcoming])
- Discuss pivot tables in Excel [Tentative]
- /Day 8 notes
Week 8: May 15 [Tentative]
Today we'll be introducing two additional web APIs (still [To Be Decided]) but we're considering Yelp, Reddit, and Twitter.
- [To Be Decided]
- [To Be Decided]
Week 9: May 22 [To Be Decided]
Today we'll be talking about doing visualization directly in Python.
- Visualization dos and don'ts. We'll discuss the European Environmental Agency's list of advice for making charts. **I will refer to this guide as a grade your final projects.**
- Two options for remainder of class. You can work through this introductory guide to visualization in python or you can work on your final project. I'll be here to answer any questions.
Optional visualization in python tutorial Self-guided visualization tutorial in python. Download here. Save the file in a new directory in your desktop and open it with jupyter notebook
If you are on Windows, you may run into an issue with missing path variables. This SO post helped me solve it.
Week 10: May 29 (NO MEETING) [Tentative]
Because of memorial day, there will be no class this week.
In lieu of class, we will arrange to have a virtual final presentations this week. This will involve (a) posting a short video of you presentation and (b) giving feedback to some numbeer of your classmates. Many of the details are still to be decided.
Teaching and learning in a pandemic
The COVID-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote, synchronous, and asynchronous instruction and the fact that many of us will not be anywhere near campus or each other this year. These will reshape our collective "classroom" experience in major ways.
On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.
I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.
- This text is borrowed and adapted from Aaron Shaw's statistics course.
Your Presence in Class
As detailed in section on case studies and in my detailed page on assessment, your homework in the class is to prepare for cases and case discussion is an important way that I will assess learning. Obviously, you must be in class in order to participate. In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc.
There are many students who have eagerly requested to join the class, but there are not enough seats. I want to include as many students in the class as possible, we will automatically drop anyone who misses the first two class sessions and try to replace them with unenrolled students who do attend. This is consistent with college policy and with the course description in the catalog.
[To Be Decided]
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.
The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/ Safety
Call SafeCampus at 206-685-7233 anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.
This includes: cheating on assignments, plagiarizing (misrepresenting work by another author as your own, paraphrasing or quoting sources without acknowledging the original author, or using information from the internet without proper citation), and submitting the same or similar paper to meet the requirements of more than one course without instructor approval. Academic dishonesty in any part of this course is grounds for failure and further disciplinary action. The first incident of plagiarism will result in the student’s receiving a zero on the plagiarized assignment. The second incident of plagiarism will result in the student’s receiving a zero in the class.
If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to uw at your earliest convenience so we can discuss your needs in this course.
If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or email@example.com or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.
Other Student Support
Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact the graduate program advisor for support. Furthermore, please notify the professors if you are comfortable in doing so. This will enable us to provide any resources that we may possess (adapted from Sara Goldrick-Rab). Please also note the student food pantry, Any Hungry Husky at the ECC.
Credit and Notes
This class has been taught at UW in several forms and this syllabuses draws heavily from these previous versions. Syllabuses from earlier classes can be found online at:
- Community Data Science Course (Spring 2017) taught by Tommy Guy
- Community Data Science Course (Spring 2016) taught by Tommy Guy
- Community Data Science Course (Spring 2015) taught by Benjamin Mako Hill
- Community Data Science: Programming and Data Science for User Experience Research (Spring 2016) by Jonathan Morgan
- Human Centered Data Science which was developed by Jonathan T. Morgan, Brock Craft, and Cecilia Aragon with contributions by Os Keyes and Brandon Martin-Anderson. That class was taught three times:
All of these classes were strongly based on the curriculum developed as part of the Community Data Science Workshops which were organized and developed by by Benjamin Mako Hill, Ben Lewis, Frances Hocutt, Jonathan Morgan, Mika Matsuzaki, Tommy Guy, and Dharma Dailey. The workshops have been designed with lots of help and inspiration from Shauna Gordon-McKeon and Asheesh Laroia of OpenHatch and lots of inspiration from the Boston Python Workshop.