STAT 336: Data Communication and Visualization

About the Course

Instructor

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

Office hours/student 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

Tuesday/Thursday 3:25-5:00 pm, OSS 431

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 STAT 201 or STAT 220 or STAT 314 or MATH 303

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 [40%]: There will be five mini-projects throughout the semester: Basic Viz, Dear Data, One Number Story, Lightning Talk, and the Community Partner Project.
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. I may adjust the weighting of different aspects as the course progresses. 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 [5%]: Two short, timed, conceptual quizzes on material from readings and baseline technical skills.
Labs [10%]: Throughout the semester, you will be developing new technical skills using R, Tableau, spreadsheet software, and more. There will be regular lab assignments graded on a complete/incomplete basis.
Final Project [20%]: The final project will see you applying what you have learned to a dataset of your choice. There will be several milestones along the way to help you prepare, and we will hold a demonstration session during the scheduled “finals” period for class.

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 late, but if you miss the peer review, you will not get credit for the first draft or 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 with the Lumen Christi Catholic Community (LCCC). 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 Counseling and Psychological Services (CAPS) or by calling (651) 962-6750. Full-time students are eligible for all CAPS 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. You are probably not alone. There’s a reason that this link is on the Dean of Students Office’s website: https://www.stthomas.edu/deanofstudents/foodinsecurity/. 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 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.

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.

Week	Topic	Lab	Deliverables
1: 2/5-2/9	Introduction to the course		Introductions
2: 2/12-2/16	Archives visit, Intro to viz	Introduction to R, ggplot2	Basic viz (first draft)
3: 2/26-2/23	Art and handmade viz	Handmade viz lab	Basic viz (peer review), Dear data (idea)
4: 2/26-3/1	Color	Ugly plot	Basic viz (final draft), Dear data (sketches)
5: 3/4-3/8	Writing about data	Data exploration lab	Dear data (final)
6: 3/11-3/15	Perception	Transform/tidy lab	One number story (first draft)
7: 3/18-3/22	Speaking about data	Finding data lab	One number story (peer review), One number story (final draft)
8		Spring break
9: 4/2-4/5	Speaking about data	Intro to Tableau lab	Lightning talk (first draft), Lightning talk (peer review)
10: 4/8-4/12	Uncertainty	Improve a graph lab	Lightning talk (final draft)
11: 4/15-4/19	More variables	Text lab	Final project (initial idea), Community partner project (initial idea)
12: 4/22-4/26	Visualizing time	More Tableau lab	Community partner project (first draft), Community partner project (peer editing)
13: 4/29-5/2	Visualizing space	Interactive and/or spatial graphics lab	Final project (data)
14: 5/6-5/10	Weird stuff	Inkscape lab?	Community partner project (second draft), Final project (visualization draft)
15: 5/13-5/16	Work time
	Finals week

*these items are assumed each week going forward

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.