DASC 336: Data Communication and Visualization

About the Course

Instructor

Dr. Amelia McNamara (she/her), amelia.mcnamara@stthomas.edu

Office hours: Mondays 11 am - noon in person (OSS 407) or via Zoom, Fridays 2-3 pm on Zoom only, and by appointment. Links available in the first Module of the course.

Course format

This course is scheduled as live and in person. I am not providing a Zoom option this semester.

Covid-19 circumstances: At St Thomas, we are committed to a culture of care for all. If you are spending time on campus, you are expected to abide by the campus preparedness plan. I will be continuing to mask in our classroom, and would appreciate if you did the same. However, masks are no longer required on campus.

If anything changes regarding the format of the course, I’ll keep you informed through course announcements.

Class meetings

Monday/Wednesday 2:55-4:35 pm, OSS 428

Course description

This course will prepare students to effectively communicate the insights from data analysis. The course will cover the three main methods of communicating information about data—visually, orally, and in writing. Students will learn to tailor their communication to their audience and create publication-ready and boardroom-ready presentations of their results.

Prerequisites: CISC 130 or 131; and DASC 112, DASC 120, STAT 303, or STAT 314.

Course Goals

Learn appropriate methods for visualizing and communicating data, both numerical and categorical.
Develop technical skills using spreadsheets, Tableau, and R, to visualize and communicate data.
Apply course material to communications you find in the wild, and datasets that interest you.

Textbooks

There is no required textbook. Every week there will be readings provided from a variety of texts, including:

Communicating with Data: The Art of Writing for Data Science (Deborah Nolan and Sara Stoudt)
Data Feminism (Catherine D’Ignazio and Lauren Klein)
Numbers in the Newsroom (Sarah Cohen)
The Functional Art (Alberto Cairo)
Visualize This (Nathan Yau)
How to Lie with Statistics (Darrell Huff)
Living in Data (Jer Thorp)
The Visual Display of Quantitative Information (Edward Tufte)

and more.

Grading

Grade breakdown

Mini-projects 30%: There will be three mini-projects throughout the semester: Visualization in the Wild, One Number Story, and the Community Partner Project. Each project is worth 100 points. There are scaffolding assignments that develop each mini project, and these scaffolding assignments are graded on a credit/no credit basis. If you do not get credit for a scaffolding assignment, your mini-project score will be dropped 10 points. For example, if you do not submit a first draft or participate in peer review for the One Number Story, your maximum score on the One Number Story will be 80.
Reading and participation 25%: Each week, you will read the assigned reading and annotate Perusall as you go. There are several ways to earn full credit on Perusall, including high quality comments, making lots of comments, answering questions, upvoting, and more. In addition to engaging with the reading, full course participation includes attendance in class, engagement with in-class discussions, and substantive participation in peer reviews.
Quizzes 10%: Two short, timed, conceptual quizzes on material from readings and baseline technical skills.
Weekly viz/exercise 15%: Almost every week we will make a data visualization. This weekly viz will be graded on a complete/incomplete basis. Occasionally, a week does not have a visualization component and the weekly viz may be replaced with a different type of exercise.
Final project 20%: The final project will see you applying what you have learned to a dataset of your choice. Like the mini projects, the final project is worth 100 points, and scaffolding assignments are graded on a credit/no credit basis. If you do not complete a particular scaffolding assignment, your project score will be dropped 10 points.

Late work

In general, I am very lenient with extensions (particularly during covid), however much of this course depends on work done as a group, such as peer reviews of drafts, and in-class discussion. I will give credit for late assignments only if they do not interfere with this type of synchronous process. For example, you could turn in the One Number Story (first draft) late, but if you miss the peer review, you will not get credit for the peer review element. Please let me know if you are having trouble meeting deadlines, as I may be able to move things for the entire course if that is appropriate.

Grade return policy

I strive to return work to you within a week of the submission deadline, or no more than two weeks later. Work submitted past a deadline may have a longer turnaround time.

Policies and Expectations

Common Good Community-Engaged course

This course has been designated as a Common Good Community-Engaged course through the Center for the Common Good. As we examine issues related to statistical communication we will put them into perspective by engaging in service learning in collaboration two community partners: Lumen Christi Catholic Community (LCCC), and Pillsbury United Communities (PUC). You will be engaged in learning outside the classroom, which will enable you to apply your academic knowledge to the “real life” client and situation. Through this collaboration, you will be given opportunities to improve your professional skills, and learn how to be active participants in your discipline and your community – all for the common good.

Inclusive classroom

Because data is collected by and about humans, it always encodes our viewpoints and biases. As a result, this course will likely touch upon difficult topics related to race, gender, inequality, class, and oppression.

We each come into this class with different perspectives that can be shared to enhance our understanding of these issues. I ask that you enter these conversations with respect, curiosity, and cultural humility. You should be open to alternative perspectives and willing to revise beliefs that are based on misinformation. As a general rule, your ideas and experiences can always be shared during these conversation but refrain from dismissing the experiences of others.

Please plan to treat me and your classmates with respect. To me, respect includes using peoples’ pronouns and preferred name (both for me and for your classmates), arriving at class on time or coming in quietly if you are late, and focusing on the task at hand. Of course, personal attacks of any kind will not be tolerated.

Stressor Statement

The course content may elicit strong reactions from some students, and it is understandable that some may feel uncomfortable discussing these issues in class. Please take the time to care for yourself. If you are struggling with personal issues, or find the content of course overwhelming, please seek assistance at the Center for Wellbeing or by calling (651) 962-6750. Full-time students are eligible for health services including individual counseling sessions.

I also encourage you to speak with me. You do not need to share why a topic may be overwhelming, but by alerting me that you may not be able to participate during particular discussions, we may be able to find a way to avoid those topics, or can then work together to find alternate ways for you to share your ideas instead of verbally expressing them in class.

Bias Reporting

St. Thomas is committed to providing an inclusive living, learning and working environment that supports the well-being of each member and respects the dignity of each person. Incidents of hate and bias are inconsistent with the St. Thomas mission and convictions and have no place here. If you are a student who has experienced or witnessed a bias or hate incident, we want to address the incident and provide you with resources. Go to the Bias or Hate Reporting website to get more information and to make an online report.

Sexual Harassment and Title IX Reporting

The University of St. Thomas mission and convictions embody our commitment to promote and protect the personal dignity and well-being of every member of the St. Thomas community. Sexual harassment, sexual assault and other forms of sexual misconduct are antithetical to the commitment, and they constitute unlawful sex discrimination. All forms of sexual misconduct are prohibited by St. Thomas.

I am a responsible employee when it comes to reporting sexual violence. That means I am required to report certain incidents to the Title IX Coordinator. Our school cares about the safety of our students and has created this requirement because sexual violence, in all its forms, is unacceptable, and we’re committed to holding perpetrators accountable and keeping survivors safe. Your privacy is of utmost importance and this institution will do everything possible to keep all reports private and only share with those who need to know. You will never be forced to share information and your level of involvement will be your choice.

For more information, please go to the St Thomas Title IX website.

Financial Hardship

Hungry? Groceries Out of Reach? Food and housing insecurity is a hidden, yet common, issue among college students. If you have difficulty affording groceries or accessing sufficient food to eat every day, or if you lack a safe and stable place to live, and if you believe this may affect your performance in this course, you are urged to contact the Dean of Students Office for support (2x6050 or deanstudents@stthomas.edu) You are probably not alone. You may also access food assistance through the Tommie Shelf Food Mobile: https://www.stthomas.edu/center- for-common-good/volunteer-opportunities/foodjusticetommieshelf/. If you feel comfortable doing so, please notify me so that I may connect you to other resources: https://www.stthomas.edu/deanofstudents/foodinsecurity/.

Disability Statement

Academic accommodations will be provided for qualified students with documented disabilities including but not limited to mental health diagnoses, learning disabilities, Attention Deficit Disorder, Autism, chronic medical conditions, visual, mobility, and hearing disabilities. Students are invited to contact the Disability Resources office about accommodations early in the semester. Appointments can be made by calling 651-962-6315 or in person in Murray Herrick, room 110. For further information, you can locate the Disability Resources office on the web at https://www.stthomas.edu/student-life/resources/disability/.

Collaboration and generative AI

The goal of this course is to help you develop and strengthen your skills as a data communicator. It is acceptable to work collaboratively with other students, or use generative AI (such as ChatGPT) to help you understand topics, debug code, and clean up written work. However, I expect all work submitted to be substantively yours. This means that copying and pasting large blocks of code or major textual elements from another student, internet websites, or generative AI systems, is not acceptable. If I suspect work you submitted is not substantively yours, I may ask you to come in and discuss the work with me. If you cannot explain how you arrived at the solution, and restate the communication in your words, you will receive no credit. All students are bound by the Undergraduate Student Academic Integrity Policy. Cases of dishonesty, plagiarism, etc., will be reported to the dean.

Resources

Course website and other technology

Canvas will be regularly updated with lecture handouts, project information, assignments, and other course resources. Reading discussion will take place on Perusall.

This course will include several pieces of industry-standard software, most crucially Tableau and R. No prior experience with any of the technologies is required for this course.

Both these tools have a way to use them online, but will be most useful if installed locally on your computer. R and RStudio are free and open source, and Tableau is providing free student licenses for my class. I will provide installation instructions, as well as some walkthrough videos. If you run into any problems with installation, please let me know. I have many potential workarounds.

Tentative Schedule

The following is a brief outline of the course. Please refer to the weekly modules for more detailed information, and the Academic Calendar for important enrollment dates.

Week	Topic	Weekly viz	Projects/additional assignments
1: 9/2-9/6	Introduction to the course		Introductions
2: 9/9-9/13	Intro to viz	Basic viz
3: 9/16-9/20	History	Archives reflection
4: 9/23-9/27	Color	Ugly plot	Visualization in the wild (initial idea)*
5: 9/30-10/4	Speaking about data	Found data viz	Final project (initial idea)
6: 10/7-10/11	Perception	Barchart	Visualization in the wild (peer review)*
7: 10/14-10/18	Uncertainty	Multiple variables	Final project (data)
8: 10/21-10/25	Handmade visualization	Hand-drawn viz	Community partner project (initial idea), Quiz 1
9: 10/28-11/1	Simplification	Improved graph	Final project (visualization draft)
10: 11/4-11/8	Writing about data	Data exploration	One number story (first draft)
11: 11/11-11/15	More variables	Interactive viz	One number story (peer review)
12: 11/18-11/22	Space	Map	Community partner project (first draft)
13: 11/25-11/29	Time	Time series	Community partner project (peer editing)
14: 12/2-12/6	Weird stuff	Inkscape?	Community partner project (second draft)
15: 12/9-12/13	Work time?		Quiz 2
December 18, 1:30-3:30 pm	Finals week		Final project (final draft), Community partner project (final draft)

Each week also includes a reading assignment in Perusall. Typically, reading assignments are due at the start of class on Monday (2:55 pm).

*Visualization in the wild will take place over many weeks, so these deadlines may be different depending on the student.

Acknowledgments

Some of the materials used in this course are derived from lectures, notes, or similar courses taught elsewhere. Particular thanks to Jordan Crouser and Mark Hansen for their materials and inspiration.