Syllabus
36-315: Statistical Graphics and Visualization, Summer 2026
Basic Information
Instructor
Quang Nguyen (quang@stat.cmu.edu)
Lectures
MWF 12:30–1:50pm, SH 234
Labs
TR 12:30–1:50pm, BH 140B
Office Hours
W 2–3pm
Website
36-315-summer26.netlify.app
All resources (lectures, assignments, etc.) are posted on this website.
Assignments must be submitted on Gradescope, unless otherwise noted.
Textbooks
None required. Lecture slides will be posted. Other references will be provided throughout the course.
Software
R (programming language), RStudio (IDE), and Quarto (reproducible documents)
Weekly Assignments
Labs are due on Tuesdays/Thursdays by 11:59pm.
Homeworks are due on Fridays by 11:59pm.
Graphic Critiques
Two graphic critiques, due Wednesday, May 27 and Wednesday, June 10.
Take-Home Exam
One take-home exam on Tuesday, June 2.
Final Project
Includes a report and a video presentation, both due Wednesday, June 18.
Grading
25% Homework
15% Lab
20% Take-Home Exam
10% Graphic Critique
30% Final Project
Final grades are computed based on the weighting scheme above.
No assignments are dropped when calculating grades.
Final letter grades will be based on the standard 10-point scale (A: 90–100, B: 80–89, C: 70–79, D: 60–69, R: 0–59). Grades will be rounded, such that an 89.50% would be an A, but an 89.49% would not.
Final semester grades may be curved, but the curve will never lead to a worse grade than the 10-point scale. For example, a grade of 85% would earn you at least a B, and a curve could only improve it.
Overview
Description
Graphical displays of quantitative information take on many forms, and they help us understand data and statistical methods by (hopefully) clearly communicating arguments, results, and ideas. This course introduces students to the most common forms of graphical displays and their uses and misuses. Students will learn how to create these displays and understand them from a statistical perspective. Furthermore, the course will consider complex data structures that are becoming increasingly common in data visualizations (temporal, spatial, and textual); we will discuss common ways to process these data that make them easy to visualize. All assignments will be in R; although this is not a programming class, using software like R is essential to create modern-day graphics. Throughout, communication skills (visual, written, and oral) will play an important role. Indeed, if it’s true that “a picture speaks a thousand words,” then ideally the one thousand words you are communicating with your graphics are statistically correct, clear, and compelling.
Learning Objectives
1. Understand the fundamentals of data and reproducible workflows
Distinguish between data types and pinpoint which graphics and analyses are appropriate for a particular data type.
Write readable, reproducible code to explore data visually.
Master the use of R, RStudio, Quarto, and other tools to promote reproducible research and allow others to build from your work.
2. Create high-quality statistical graphics
Master the use of ggplot2 to create statistical graphics that are easily readable and understandable for technical and non-technical audiences.
Incorporate statistical information (e.g., the results of statistical tests or uncertainty quantification) into elegant data visualizations.
3. Assess and critique statistical graphics
Review statistical graphics objectively and professionally.
Describe the pros and cons of a given graphical choice.
Give useful suggestions for improvement.
4. Write and communicate about statistical graphics and data visualizations
Describe graphics concisely and accurately to technical and non-technical audiences.
Incorporate appropriate statistical language in descriptions of graphics.
Components
Lectures
Almost all course content is covered during lecture. If you miss a lecture, you are responsible for the materials covered.
Please participate and ask questions during lecture. And please make a habit of showing up on time. Consistent tardiness is not acceptable and may lead to grade deductions.
Labs
Labs will involve exercises (usually a mix of coding and written responses) related to the materials covered during lectures. Each lab will be turned in as a lab assignment. Labs are designed to be completed during the session, but they are not due until 11:59pm on the day of the lab, so students have extra time if needed. Each lab must be completed in a Quarto document and submitted on Gradescope as a pdf file.
Lab attendance is mandatory. If you submit a lab assignment but did not attend its lab session, there is an automatic 20-point deduction. As with lectures, please arrive to labs on time.
If you need to miss a lab for a valid reason, contact the instructor at least 24 hours before the lab to discuss doing the lab on your own and getting full credit. If there is an unexpected emergency, notify the instructor immediately. It is up to the instructor to determine if an accommodation is possible.
Homeworks
Homework assignments are due on Fridays by 11:59pm, and are posted on Mondays of the same week. Each homework must be completed in a Quarto document and submitted on Gradescope as a pdf file. Be sure that your code, graphs, and answers are properly displayed on your homework before submitting.
Graphic Critiques
You will submit two critiques of data visualizations you find in the wild. These must be from a recent source that was posted online for the first time within the past year (i.e., not from some 2011 blog post) and cannot be an example from lectures, labs, or homework.
Each graphic critique must be completed in a Quarto document and submitted on Gradescope as a pdf file. You will get practice doing this on the first homework. Then, you will submit two graphic critiques during the semester. The due dates for each critique are Wednesday, May 27, 11:59pm and Wednesday, June 10, 11:59pm. These are meant to be light and entertaining assignments.
Take-Home Exam
There is one take-home exam on Tuesday, June 2. The exam will assess students’ ability to apply course materials (covered to date) to answer open-ended questions about a real dataset. Students will have from 9am to 11:59pm on Tuesday, June 2 to complete the exam. The exam is designed to take 2–3 hours, but the 15-hour time window is only meant to provide some flexibility and alleviate time pressure. More information will be given closer to the exam date.
Final Project
The final project will assess students’ data communication skills (visual, written, and oral). Students will work individually to create high-quality graphics using a dataset of their choosing. The project will culminate in a written report and a short video recording. More information will be provided later.
Logistics and Policies
Regrades
Any regrade requests for lab and homework assignments must be submitted on Gradescope within 48 hours of grade release. Do not submit a request via email. If a regrade request is submitted more than 48 hours after grade release, it will not be processed.
Extensions
In general, late submissions will not be accepted. In case of truly exceptional situations (such as illness or other emergencies), the instructor can make exceptions and allow late work. Note that file rendering issues or technical difficulties with submission on your end will not receive extensions.
To submit an extension request, you must email the instructor at least 36 hours before the due date. It is up to the instructor to decide if an extension may be granted.
Disability Services
If you require a special accommodation, please visit the Office of Disability Resources. No accommodations can be provided without proper documentation.
Integrity
Cheating, plagiarism, and/or unauthorized assistance on assignments will be dealt with in accordance with the CMU Policy on Academic Integrity.
Collaboration
You are encouraged to discuss assignments with your classmates. But all the work you turn in must be your own. You must independently write your own code, generate your own graphics, and write your own solutions and reports. Copied or nearly identical submissions will be considered cheating and plagiarism.
External Sources
External sources (e.g., books, websites, etc.) should only be used to support your work, not to obtain your work. You may use external sources to look up code documentation, find useful packages, find explanations for error messages, or clarify lecture materials. You may not use them to copy code, text, or graphics without proper attribution. Do not copy old solutions, and do not consult or read them.
Generative AI
When allowed, you may consult generative AI tools. However, you must take intellectual responsibility for your code, analysis, and writing, and be able to explain and defend every decision. You must do the thinking, not generative AI. You may not use generative AI to come up with analysis or interpretation for you, only to augment your own work. If you use a generative AI tool, you must disclose and explain what you use it for.
Calendar
(subject to change)
| Date | Session | Assignment |
|---|---|---|
| Week 1 | ||
| May 11 (Monday) | Lecture 1 | |
| May 12 (Tuesday) | Lab 1 | Lab 1 due |
| May 13 (Wednesday) | Lecture 2 | |
| May 14 (Thursday) | Lab 2 | Lab 2 due |
| May 15 (Friday) | Lecture 3 | Homework 1 due |
| Week 2 | ||
| May 18 (Monday) | Lecture 4 | |
| May 19 (Tuesday) | Lab 3 | Lab 3 due |
| May 20 (Wednesday) | Lecture 5 | |
| May 21 (Thursday) | Lab 4 | Lab 4 due |
| May 22 (Friday) | Lecture 6 | Homework 2 due |
| Week 3 | ||
| May 25 (Monday) | Memorial Day | |
| May 26 (Tuesday) | Lab 5 | Lab 5 due |
| May 27 (Wednesday) | Lecture 7 | Critique 1 due |
| May 28 (Thursday) | Lab 6 | Lab 6 due |
| May 29 (Friday) | Lecture 8 | Homework 3 due |
| Week 4 | ||
| June 1 (Monday) | Lecture 9 | |
| June 2 (Tuesday) | NO LAB | Take-home due |
| June 3 (Wednesday) | Lecture 10 | |
| June 4 (Thursday) | Lab 7 | Lab 7 due |
| June 5 (Friday) | Lecture 11 | Homework 4 due |
| Week 5 | ||
| June 8 (Monday) | Lecture 12 | |
| June 9 (Tuesday) | Lab 8 | Lab 8 due |
| June 10 (Wednesday) | Lecture 13 | Critique 2 due |
| June 11 (Thursday) | Lab 9 | Lab 9 due |
| June 12 (Friday) | Lecture 14 | Homework 5 due |
| Week 6 | ||
| June 15 (Monday) | Lecture 15 | |
| June 16 (Tuesday) | TBD | |
| June 17 (Wednesday) | TBD | Project due |