PSYC 81.09: Storytelling with Data (Spring 2026)
| Meeting times | M/W/F 10:10 — 11:15 AM |
|---|---|
| X-hour | Th 12:15 — 1:05 PM |
| Classroom | Moore 302 |
| Instructor | Dr. Jeremy R. Manning |
| jeremy@dartmouth.edu | |
| Office Hours | By appointment on Tuesdays |
Course Description
Humanity creates nearly 350 TB of data each day, and much of it is freely available for us to download. But making sense of that staggering amount of text, video, audio, and other forms of data is like drinking from a firehose: the sheer quantity of data makes it tricky to make sense of what it means. In this course we will study how to find, clean, analyze, make sense of, and communicate about complex data.
In addition to helping you build up your data science toolbox, the course is centered around telling engaging, compelling, (often) entertaining, and, above all, clear "stories" about data. This will require considering your audience's mindset, goals, motivations, and backgrounds, and then leveraging those considerations to develop effective ways of conveying the major plot points.
Course Goals
The primary goals of this course are to help you develop intuitions and tools for (a) finding meaningful patterns in complex datasets and (b) communicating your findings in an intuitive way. To that end, you will be learning to interact with AI coding agents, analyze and create plots, and present your work effectively. I see data interpretation and visualization as a deep psychological issue: creating compelling figures and stories is just as much about theory of mind of the receiver as it is about accurately reporting the deeper truths underlying the dataset. We will therefore motivate the tools and skills we develop from a psychological and neuroscientific perspective. My hope is that you will draw heavily on what you learn in my course in "real life," in whatever fields, professions, and/or hobbies, you choose to pursue.
Pre-Requisites
You should have taken a course on statistics or probability (e.g. PSYC 10, AP Stats, or similar). An online statistics course is fine as a stand-in, but I will expect you to know about hypothesis testing, probability distributions, and have some intuitions about how to design and interpret statistical analyses before you start this class. You will also benefit from prior work in experimental design, such as PSYC 11 or similar. Additional prior course work or experience in Psychology, Neuroscience, Computer Science, Math, Engineering, Creative Writing, Theater, and/or Graphic Arts will also be useful.
In defining these pre-requisites somewhat vaguely, I hope we will end up with a group of students with a diverse array of backgrounds, interests, and skills. Some students may be more "quant" minded (they might have an easier time learning and applying the programming and analysis components of the course) and others will be more "humanities" minded (they might have an easier time crafting and presenting compelling stories). However, although you do not need to know how to program before you start this course, every student will eventually need to both program analyses and present results to the class. Ultimately we'll all build on our existing skills and experiences to help each other grow intellectually and learn new practical skills.
Course Materials
All required text, tutorial, and audiovisual materials will be freely available to you throughout the term. You do not need to purchase any additional materials to fully participate in this course. A complete outline, with links to all materials, assignments, and lecture recordings, may be found on the course website.
You will also need an internet-enabled computer or tablet capable of displaying and outputting graphics and running a standard web browser (e.g., Firefox or Chrome). You'll use your computer to participate in class meetings, engage with self-paced learning materials, and complete your assignments.
In addition to the above required materials, you may optionally find it useful to read through one or more of the following classics in data visualization by Edward Tufte:
- The Visual Display of Quantitative Information (ISBN: 0961392142)
- Envisioning Information (ISBN: 0961392118)
- Visual Explanations: Images and Quantities, Evidence and Narrative (ISBN: 0961392126)
Purchasing these books is optional, and we will not even be referencing them directly in the course. However, I am including them in our syllabus because I have found the books to be particularly excellent for drawing inspiration from a true master of data visualization.
Format and Overview
The first 4 weeks of the course are primarily instructor-directed, supplemented by in-class discussions and assignments. We'll cover four themes: (1) What makes a good story?, (2) Visualizing data, (3) Python and Jupyter notebooks as a medium for data storytelling, and (4) Data science tools. These weeks will introduce you to the fundamentals of storytelling with data.
New in 2026: We'll be using vibe coding — AI-assisted programming — to build projects and explore data. You'll learn to direct AI coding agents (like Claude Code) to create software by describing what you want in natural language.
Our course incorporates a Social Impact Practicum component in week 4. This year we'll be partnering with The Collaborative to examine and tell stories about their data.
During the second phase of the course (weeks 5–10), you (either in groups or individually) will download datasets, analyze them, produce figures or other representations, and informally present your results as "data stories" to the class each week as YouTube videos. We will then constructively critique and make suggestions about each others' stories, helping each other to become better data storytellers.
Schedule
| Week | Dates | Theme |
|---|---|---|
| 1 | Mar 30 – Apr 3 | Truth & Storytelling (W/Th: no class) |
| 2 | Apr 6 – 10 | Visualizing Data |
| 3 | Apr 13 – 17 | Programming + Vibe Coding |
| 4 | Apr 20 – 24 | Social Impact + Data Science Stack (W: no class) |
| 5 | Apr 27 – May 1 | Part II begins |
| 6 | May 4 – 8 | Part II |
| 7 | May 11 – 15 | Part II |
| 8 | May 18 – 22 | Part II |
| 9 | May 25 – 29 | No class |
| 10 | Jun 1 – 3 | Part II (final week) |
Weekly Pattern (Weeks 5–10)
- Monday: Review and discuss data stories
- Wednesday: New tools + demos
- Thursday (X-hour): Additional office hours (sign up) or hackathons/demos
- Friday: Hackathons + open discussion/brainstorming
Assignments
| Assignment | Released | Due | Description |
|---|---|---|---|
| 1 | Fri Apr 3 | Mon Apr 6 | Tell the class a 5-minute story |
| 2 | Fri Apr 10 | Mon Apr 13 | Data story remix |
| 3 | Fri Apr 17 | Mon Apr 20 | Build something cool (vibe coding demo + workflow video) |
| 4 | Fri Apr 24 | Mon Apr 27 | Tell a "real" story about data |
Any assignment may be re-done at any point in the course (up to the last minute of the last day we'll meet this term — 11:15am on Wednesday, June 3). If you choose to hand in a new version of any assignment, only the latest version will "count" towards your final grade.
Tools
Dartmouth AI Tools
We will use chat.dartmouth.edu and claude.dartmouth.edu — free AI tools available to all Dartmouth students — for vibe coding, brainstorming, and analysis. These tools provide access to Claude, ChatGPT, Mistral, and other AI models.
Slack
We will use Slack (a tool for organizing notes, files, and conversations) to provide a forum for asking and answering questions, posting demos, etc. You will need to join the class workspace at the beginning of the term.
GitHub
We will use GitHub to manage and share data and code. GitHub provides an easy way of managing multiple versions of data and code that may be easily shared and tracked. To use GitHub, you will need to create a (free) GitHub account. After the second assignment, all other coursework will be submitted via GitHub.
Google Colaboratory
We will use Google Colaboratory to develop and program analyses. This tool provides a single means of organizing notes, code, and graphics in a single cohesive format ("notebook"). The programming environment is accessed via a web-browser.
Grading
All course assignments will be assigned a point value, added together, and converted to the nearest equivalent letter grade as follows (all scores in parentheses are percentages of the total possible number of points): A (95–100); A- (90–94); B+ (87–89); B (83–86); B- (80–82); C+ (74–79); C (57–73); C- (50–56); D+ (44–49); D (37–43); D- (30–36); or F (0–29).
Most of the course assignments will take the form of a YouTube video presenting a story, often with an accompanying "notebook" containing the associated code and data. You and your peers will provide feedback to each other (both formally and in class discussions). Your self assessments, peer evaluations, and my feedback will all contribute to your grade for each "story" assignment.
Scheduling Conflicts
This class works best when students are participating synchronously. When you are able, I expect you to attend and be on time for every class meeting. However, I also understand that your personal circumstances (e.g., health concerns, religious observances, other commitments, emergencies, etc.) may limit your ability to participate synchronously.
If you are generally able to attend the synchronous course meetings, I would appreciate a heads up (as far in advance as you're able to tell me) if you know you will be missing class. That will help me prepare and (and, if necessary, reconfigure) group activities, etc.
Importantly, however, no part of your grade will depend on attendance. I trust that you will make the best decision for yourself, based on your personal goals and circumstances, about whether or not to attend each class meeting. By the same token, if you do miss a class meeting, it is your responsibility to make up the material.
Student Needs
I strive to maintain a welcoming and accessible classroom environment. I want you to be an active participant and contributor to ongoing discussions and activities, and that means that every student should feel comfortable in my classroom. If you would like me to be aware of any issues that arise during the term, or any personal needs that may require adjusting how I run my class or how you participate, I encourage you to reach out to me privately. Dartmouth's Student Accessibility Services Office can also help assist with setting up disability-related accommodations.
The Academic Honor Principle
I expect you to abide by Dartmouth's Academic Honor Principle. This course is a bit different from some others, in that I encourage (and expect) you to discuss your assignments with your classmates. The class will be heavily collaborative, and I encourage group presentations and collaboration on assignments. You can also use "AI" tools like chat.dartmouth.edu to help analyze your datasets, come up with story ideas, flesh out your writing, and so on. In short, you are encouraged to use any and all tools and resources at your disposal to enable you to do your best work.
The above said, note the emphasis on "your" in the previous sentence. It's important that you contribute something substantive to each project that you participate in, and that you actively engage with the material. Copying and pasting from another student, or from ChatGPT (et al.) will not fulfill your assignment requirements. Similarly, you cannot "re-use" projects from other courses without modifying them, although some projects will allow you to (optionally) build on prior work.
Put simply, you should hand in your own (new) work, even if you collaborated or discussed your assignment with a classmate, used other resources or tools, and so on. If you have any questions about the Academic Honor Principle and how it applies generally to this course, or specifically to a particular assignment, please ask me.