Interactive Data Science and Visualization
Formal curriculum and syllabus which is completed by the course manual with practical guidance on either taking the course as a learner or providing / adapting it as an instructor.
Other formats: Markdown version | PDF version (from UC Berkeley Spring 2025 teaching)
Contents
Motivation
Before getting into a detailed breakdown of the material, let's explore how this course can help.
Why take this class?
Data are essential to how we experience and understand the world. The way we interact with tools, analyses, and simulations all change the insights we learn, the stories we tell, the actions we take, who is included, and what is left out. This course combines traditional user experience and information design with interactive storytelling and video game design. We will emphasize practical techniques and technical skills needed to make both world-class data visualizations and interactive data science tools. More info online at https://interactivedatascience.courses.
Who is this for?
Scientists, engineers, designers, artists, journalists, and anyone else who wants to explore new methods for interactively and visually understanding, experiencing, and communicating data.
What we will do together?
We have three pillars of our time together:
- Explore data visualization theory and fundamentals before studying cutting edge work in interactive data science.
- Learn practical career-building technologies for building both static and interactive data experiences through hands-on portfolio-building activities.
- Examine existing powerful and informative interactive experiences including data visualizations and other interactive media like video games.
What you will be able to do after?
After completing the course, you should be able to:
- Build data visualizations and other interactive experiences to share your data findings with others.
- Invite your audience in as co-creators to build new meaning alongside you in your work.
- Craft digital tools which help both you and your users navigate data-heavy tasks and uncover insights.
- Tell impactful stories that engage your readers emotionally through data.
- Incorporate ethics and accessibility into your work.
Basics
Next, let's explore the logistics and basic information of the class together. This section offers fundamental information about the course as required to consider its place within a personal learning journey or for instructors to consider its role within a broader degree plan.
Instructor
Hello! My name is Sam Pottinger. I am a Senior Research Software Engineer / Data Scientist at UC Berkeley's Schmidt Center for Data Science and Environment. I've previously worked at Google, Apple, two start ups, and global design firm IDEO. I offer over a decade of data visualization / interactive science experience. My research at Berkeley blends information design, video game design, and ML / AI, and public policy for environment.
Schedule and credit
This course can be adapted to meet different learning criteria and formats. However, in teaching it in person at the University of California Berkeley, we met twice weekly for an hour with an additional hour of office hours online. It was completed in a semester-based schedule. Additionally, this was originally offered for 2 credit hours though conversion of optional aspects of the course to required may meet criteria to teach for 3 credit hours instead. In either case, this was comprised of hands-on activities, final mastery project, original lectures, discussion, hands-on skills labs, and guest lectures over 7 short modules.
Prerequisites
Recommended but not required that learners have had prior introduction to Python or JavaScript (course or self-taught). Recommended at the graduate level or for undergrad at sophomore or later.
Skills taught
In considering how this course may layer into broader degree requirements or personal learning objectives:
- Technical skills taught: Sketchingpy (Pottinger 2024), P5 (P5js Contributors 2026), and D3 (Bostock et al 2011). Will also provide optional brief Python and JavaScript / HTML / CSS instruction.
- Theoretical skills taught: Information design, video game design, evaluative methods, human visual perception, accessibility.
Grading
Given each lecture as described below. 54% small projects, 12% interactive experience reflection, 35% final project intended to build mastery. Originally taught as pass / fail. Scores provided for each small project, final project, and the interactive experience reflection.
Additional Resources
Includes complementary short reading / videos and supportive technology skills labs. The interactive experience will be your choice of either a video game or interactive data visualization.
Course sections
The course is designed across 7 major sections expected to be taken in order.
1. Hello
Begin with an initial exploration of key ideas.
We start our journey together by motivating why data visualization is useful, what interactive science can offer, and take an early look at some of the foundational ideas that we will explore together throughout the rest of the course. We will also make sure everyone has what they need to do some upcoming activities with two optional skills labs.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 0 | Skim the overall course materials |
Short pre-course primer on class structure, purpose, and how to use the resources. |
None. |
| 1 | None | 4 perspectives on data visualization. |
Async introductions. |
| 2 | Trying out the 4 perspectives on 4 examples. |
Example visualization 1 |
|
| 3 | Skills lab on creative coding in Python with basic concepts introduced. |
Revisit example from the 4 perspectives lens. |
|
| 4 | Skills lab on software architecture for creative coding in Python. |
None. |
2. Primitives
Study of the essential building blocks for data visualization.
We transition to the foundational building blocks of data visualization and explore the cognitive science underpinnings behind effective information design. Using what many call the grammar of graphics, we will also have our first small projects that begin to offer hands-on experiences to build custom graphics with code.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 5 | Review of Tufte's "The Visual Display of Quantitative Information" (LeRoy 2018) |
Examples of problematic graphs (Tufte 2001), Chart junk (Tufte 2001), Tuftean axes (Tufte 2001), After Tufte (Elliot 2016) |
Example visualization 2. |
| 6 | Visual processing stages, working memory, pre-attention features (Ware 2021). Gestalt / neg space (Portnow et al 2018) |
Job satisfaction. |
|
| 7 | Color (Ware 2021), Gestalt / neg space (Portnow et al 2018) |
Job satisfaction remix. |
|
| 8/9 | Stroke, fill, primitive shapes, options for input, fonts. |
Responsive art 1. |
|
| 10 | Events, user loops, and data manipulation. |
Responsive art 2. |
|
| 11 | How William Cleveland Turned Data Visualization Into a Science (Pricenomics 2016) |
Cleveland and McGill (1984), grammar (Wilkinson 2005), shared axes / direct label (Tufte 2001). Chartjunk 2. |
Census 1 / Income inequality with 4 variables. |
3. Combination
Understand common patterns and methods for making new ones.
Having built up the primitives used in data visualization, we pick up the pace to build sophisticated data graphics. After using existing chart types, we venture into the great unknown by looking at how these lego pieces enable us to build completely new visual representations and how to test if novel approaches are successful.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 12 | Taxonomy of common graphs (Hess 2022). Developer happiness (Pottinger 2019), task / domain (Munzner 2014). |
Census 2 / Income inequality with 6 variables. |
|
| 13 | Movements (Harris and Kamvar 2015; Pottinger 2015). Levels (Fry 2015). Networks. Geographic data formats, projections. A look at Gephi and QGIS. |
Draw your last 5 years and your next 5 years by hand. |
|
| 14 | Task / domain (Munzner 2014). |
BART 1 |
|
| 15 | Inquiry (Munzner 2014), thinking-aloud (Lewis 1982), diary studies (Shneiderman and Plaisant 2006), participatory design (IxDF 2023), generalizable knowledge vs quality assurance. |
Think-Aloud of Pyafscgap (Pottinger and Zarpellon 2023). |
4. Conversation
Consider and design for the reader's dialogue with your work.
Just short of half way through the course, you are already an expert at choosing from existing chart types and constructing your own novel visual representations. Next we look critically at the role of the reader to construct media which enable the reader to explore more freely and become a co-creator of knowledge.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 16 | History of HCI (Duarte and Baranauskas 2016). Affordances (Norman 2013), working memory (Ware 2021), social actors (Nass et al 1994) |
BART 2 |
|
| 17 | , | Why game design and its role, thinking and play, PLATO system (Bitzer et al 1961). Ethos, pathos, logos. |
Game or interactive viz analysis part 1. |
| 18 | Bussed out visualization (Bremer and Wu 2017), Gun Deaths (Rees 2013), Parable of the Polygons (Hart and Case 2014). |
Game or interactive viz analysis part 2. |
|
| 19 | , . | Loops (Brazie 2024), Hayashida level design (Pottinger and Zarpellon 2023), Plastics (Pottinger et al 2024) |
Game or interactive viz analysis part 3*. |
5. Context
Expand our understanding of the reader.
Now that we have a deeper understanding of how to think about the reader within our designs, we next zoom out to understand users as within a broader social and physical context. We will also consider visualization in a broader sense-making context.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 20 | None | Interactive Experience graded activity. |
Interactive Visualization |
| 21 | . | Possibility space (Wright 2003). Diegesis (Dassler and Portnow 2019). APH visualization (Pottinger et al 2025). |
None |
| 22 | WCAG (MDN 2024), common patterns, non-visual strategies. |
[Final] Choose topic. |
|
| 23 | or | [Final] Initial designs. |
|
| 24 | Inquiry-based design (Hayes 2023), overview of datasets, journey mapping. |
[Final] Initial design ideas. |
6. Skills
Grow your implementation tool chest.
You have gotten through some of the most important concepts in data visualization and interactive science. Now, we will explore different technical skills as you work on your final project. Note that guest lectures may move.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 25 | None | Logistics for the final, preparing for larger or non-notebook code. Git, architecture patterns, iterative process. |
[Final] Sketch in code. |
| 26 | Learn more about ethical considerations by watching Timnit Gebru's talk on Eugenics and the Promise of Utopia through AGI. |
Explore using AI in implementing data visualizations. Optional extra lesson on using generative AI. Complete Skills Lab 6 to get hands on! |
None. |
| 26.1 | None | P5 introduction. |
[Final] Iterate for final. |
| 26.2 | Lecture 25.2 is reading for Lecture 26. |
D3 introduction. |
[Final] Iterate for final. |
7. Build
Contribute to your portfolio.
The course ends with sharing final projects and a celebration of our time together.
| Day | Reading / Watching | Classroom Material | Activity |
|---|---|---|---|
| 27 | Final reading (see appendix) |
Final projects and celebration. |
[Final] Feedback |
Details
Ready to take or teach the class? Here's some additional information that might help.
Welcoming and Supportive Environment
I love this topic and I hope you will love it too! I am dedicated to your success within this course. All activities and projects should be an engaging avenue for collaboration focused on refinement of ideas in a hands-on but low pressure setting. We will be discussing big topics through design and artistic expression. By participating, we collectively promise to generously create a welcoming and supportive environment for exploration. Lectures will be hands-on so laptops and discussion encouraged. Class time ensures successful outcomes in the course and will provide key information about assignments. However, things happen. I trust you to use your time well so attendance will not be taken. There are no textbook or service fees and free options are available for the interactive experience if you do not wish to purchase a copy of a game.
How to Succeed and Pass the Course
Except day 1, each instruction day has an outside activity essential to course objectives and complementary to that day's discussion. I am excited to provide written individual feedback to help complement other course resources. Numerical scoring is not a focus of this course. However, for the purposes of pass / fail, each instructional day has an activity worth 10 points with two instruction days per week. 14 days have small projects which are generally short to complete, the interactive experience analysis spans 3 days, and the final project spans 9 days. For each 10 point grading, 3 points are given for completeness, 3 for reflecting course materials, 2 for technical correctness, and 2 for exploration / expression. For those at UC Berkeley, 70% overall grade is required to pass (course is pass / fail).
Timely Response and Late Assignments
For those in the UC Berkeley community, being able to share timely feedback on activities enables projects to serve an important role in ensuring you get the most out of your time in the course. Therefore, activities are due at 5pm 2 days after their assignment except the final which is due altogether. Submissions must be made by May 1 if you want to receive pre-final feedback. Please talk to me if personal circumstances arise. Note that, automatically, there are no points deducted for 7 calendar days after assignment. However, 20% will be deducted each calendar day for late assignments starting at 5pm on the 8th day after assignment. All times are given in PT.
Student Feedback
At UC Berkeley, student feedback will be solicited at the end of each module to ensure the course is meeting its goals. Grades and submissions through university-provided LMS.
Contact
I work at Wellman Hall #201. Contact options available at interactivedatascience.courses. Originally taught as a small course without a TA.
Lab / Discussion
To fit the 2 credit hour format, labs will take place during course instruction time as indicated in the schedule. For those attempting a 3 credit version of the course, the lab and discussion sections can expand class time.
Paid resources
Reading to be provided. Students encouraged to purchase the interactive experience but alternatives provided. Otherwise, all materials are intended to be free to access.
See Also
For additional course materials, please see:
- Course Manual - Detailed guidance for students and instructors
- Grading Rubric - Detailed grading criteria for code projects
Works Cited
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- Tremblay, Kaitlin. "Storytelling with Verbs: Integrating Gameplay and Narrative." GDC, March 17, 2020. https://www.youtube.com/watch?v=ontNUxSLhb8.
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- ———. "Media for Thinking the Unthinkable." MIT Media Lab, April 4, 2013. https://vimeo.com/67076984.
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Interactive Experience:
- Brier, Wren, Tim Dawson, Kip Bunyea, and Erika Olsen. "Unpacking," November 2, 2021.
- Deconstructteam. "The Cosmic Wheel Sisterhood," August 16, 2023. https://cosmicwheel.com/.
- Ma, Justin, and Matthew Davis. "FTL: Faster Than Light," September 14, 2012. https://subsetgames.com/ftl.html.
- "Papers, Please," February 12, 2014. https://papersplea.se.
- Thunder Lotus Games. "Spiritfarer," August 18, 2020. https://thunderlotusgames.com/spiritfarer/.