Dr. Amelia McNamara (she/her),

Office hours/student hours: Wednesdays from 11-noon, Fridays 1:30-2:30 pm, and by appointment, on Zoom.

Course format

This course is synchronous, with hybrid options. Since the course was initially scheduled as fully in person, I am expecting most people will be in the classroom physically, but there will also be an option to attend via Zoom.

This course can also be thought of as partially “flipped.” Before our synchronous time together, you will watch video lecture snippets, and complete a readiness quiz and do discussion posts. Then, during synchronous time we will have live discussion of these topics, complete group activities, and have time to work on mini-projects.

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

Class meetings

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. This includes wearing a mask in all common spaces (including our classroom) and maintaining a 6-foot distance from others. If you feel sick, please stay home and plan to attend via Zoom.

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.


One textbook is required:

This book is available for purchase through the UST bookstore.

In addition to the textbook, there will be a number of readings provided from other texts, including:

and more.


Grade breakdown

  1. Mini-projects [40%]: There will be six mini-projects throughout the semester. The mini projects include Dear Data, Copy the Masters, One Number Story, and the Wikipedia Article. Points for mini-projects will vary depending on the complexity of the project (the Wikipedia article in particular has many steps).

  2. Discussion posts, readiness quizzes, and participation [25%]: Each week, you will need to write at least two discussion posts and complete a readiness quiz. Readiness quizzes will cover material from the reading and videos, and are designed to be easy to complete if you are prepared for class. Discussion posts allow you to expand on your understanding of topics, and will take the form of reading/video responses, and Data Diaries. On alternate weeks, you will initialize discussion posts or respond to a classmate’s. Beyond these specific items, full course participation includes attendance at synchronous sessions, engagement with in-class discussions, and substantive participation in peer reviews.

  3. Quizzes [5%]: Two short, timed, conceptual quizzes on material from readings and baseline technical skills. These quizzes will be more complex than readiness quizzes, and cover more material.

  4. Labs [10%]: Throughout the semester, you will be developing new technical skills using R, Tableau, spreadsheet software, and more. There will be periodic lab assignments graded on a complete/incomplete basis.

  5. Final Project [20%]: The final project will see you applying what you have learned to a dataset of your choice. There will be several (graded) 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 makes sense.

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

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). One useful resource CAPS offers is Let’s Talk, drop-in counseling consultation.

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 aand 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. Students can also report in person to the Dean of Students Office (room 241, Anderson Student Center) or to Public Safety.

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. If you have experienced sexual harassment/assault/misconduct based upon gender/sex/sexual orientation, and you share this with a faculty member, the faculty member must notify the Title IX Coordinator, Danielle Hermanny, who will discuss options with you. She can be reached at or (651) 962-6882. For more information, please go to the St Thomas Title IX website.

Financial Hardship

If you are experiencing financial hardship or having difficulty with access to sufficient food to eat every day, or you do not have a safe and stable place to live, please contact the Office of the Dean of Students by phone at 1(651) 962-6050 or in person in room 241, Anderson Student Center.

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 emailing For further information, you can locate the Disability Resources office on the web at


Much of this course will operate on a collaborative basis, and you are expected and encouraged to work together with a partner or in small groups to study, complete homework assignments, and work on projects. However, every word that you write must be your own. Copying and pasting sentences, paragraphs, or blocks of code from another student or the internet is not acceptable and will receive no credit. No interaction with anyone but the instructor is allowed on any exams or quizzes. All students are bound by the university Rules of Conduct. Cases of dishonesty, plagiarism, etc., will be reported to the dean.


Course website and other technology

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

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 Readings Lab Deliverables
1: 9/9 Introduction to the course N/A N/A Introductions
2: 9/14, 9/16 Writing about data CWD (ch 1, 3), How to Lie with Stats (ch 10) Spreadsheets, summaries and pivot tables First data diary, First reading/video reaction*
3: 9/21, 9/23 Journalistic structure CWD (ch 2, 7), Numbers in the Newsroom (ch 1-2) Interviewing data One number story first draft
4: 9/28, 9/30 Intro to viz CWD (8, 9), The Functional Art (intro), Visualize This (intro, ch 1) Introduction to Tableau Dear Data idea
5: 10/5, 10/7 Handmade viz CWD (ch 4), Dear Data (selections), Data Points (ch 3) Hand-drawn data viz One number story final draft
6: 10/12, 10/14 Color Design Elements (ch. 3), Envisioning Information (ch 5) More Tableau Dear Data
7: 10/19, 10/21 Perception and principles Cleveland (ch 4), Show me the numbers (ch 5), Data Points (ch 5) Introduction to R, ggplot2 Wikipedia draft
8: 10/26, 10/28 Simplification CWD (5), Show me the numbers (ch 8), Visual Display (ch. 4, 6) Simplification in ggplot2 Tableau viz, quiz 1
9: 11/2, 11/4 Speaking about data CWD (ch 11), PowerPoint Does Rocket Science More ggplot2 Copy the masters first draft
10: 11/9, 11/11 Uncertainty TBD, mostly videos dplyr and tidyr Lightning talk first draft
11: 11/16, 11/18 More variables CWD (6), Envisioning Information (ch 4), Coordinated Highlighting in Context Tableau dashboards Copy the masters final draft
12: 11/23 Visualizing space CWD (10), How to Lie with Maps (ch 2), Envisioning Information (ch 6) Lighting talk final draft
13: 11/30, 12/2 Visualizing time TBD Maps and timelines Final project first draft
14: 12/7, 12/9 Weird stuff Quiz 2
15: 12/14 TBD
Finals 12/22 Project Presentations

*these items are assumed each week going forward


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.