STAT 80B: Data Visualization
Winter 2026 Syllabus
Syllabus subject to change. First version: January 3rd 2026, Last edit on: January 6th 2026
Course Information
The course presents a comprehensive introduction to data visualization, the art and science of communicating data through visual graphics. Students will learn fundamental principles of effective visualization design, explore diverse chart types and techniques, and develop skills in creating honest, accessible, and compelling visual narratives. Based on Claus Wilke’s “Fundamentals of Data Visualization,” the course emphasizes both theoretical foundations and practical application, covering topics from basic aesthetic mappings to advanced design principles. Students will gain hands-on experience creating visualizations using industry-standard tools and will complete a capstone project demonstrating mastery of visualization principles.
Course Canvas: https://canvas.ucsc.edu/courses/89190
Instructor Information
Instructor: Marcela Alfaro Córdoba (macordob@ucsc.edu)
Students can address me as Professor Alfaro Córdoba or Dr. Alfaro Córdoba, or simply Marcela (pronounced [ m ah r s eh l ah ]). Please avoid calling me (or any of your female instructors) Miss or Ms.
A short bio: I am an applied statistician with an interest in statistical applications to environmental and biological problems. I also work on Stats and Data Science Education. I’ve been teaching at UCSC since 2021, but I have more than 15 years of experience teaching probability and statistics. If you are curious about my projects, you can check my personal webpage: https://malfaro2.github.io/
Office hours: This is a space to talk to the instructor. You don’t need to be prepared to attend; you can show up with general or specific questions. You are also more than welcome to stop by if you want to talk about a future career in Statistics or Data Science.
- Tuesday and Thursdays 3:05-3:40 in person (after class, in the same classroom)
- By appointment: Calendar appointments
Teaching Team
- TA: Jason Teng (jteng9@ucsc.edu)
- Tutor: Dominick Rangel (docrange@ucsc.edu) OH: TBD
Communication
| Course Component | Days/Times | Location |
|---|---|---|
| Lectures | TuTh 13:30 - 15:05 | Cowell Clrm 131 |
| (in person, recorded, and posted on Canvas at the end of the day) |
- Ed Discussion (see Canvas link) ← BEST option, most efficient
- Email (2-3 day turnaround, not recommended for urgent matters)
Do not use Canvas messages - you’ll get an automatic response directing you to Ed Discussion
New to Ed Discussion? Quick start guide
Learning Outcomes
By the end of the course, students should be able to:
- Identify and explain the fundamental principles of effective data visualization, including the mapping of data to visual aesthetics and the use of appropriate coordinate systems and scales
- Distinguish between different types of data (quantitative, categorical, ordinal) and select appropriate visual encodings for each
- Create effective visualizations for diverse data types including amounts, distributions, proportions, associations, time series, geospatial data, and uncertainty
- Apply appropriate color theory principles to enhance clarity, accessibility, and visual communication in data graphics
- Implement multi-panel figures and small multiples to facilitate meaningful comparisons across subsets of data
- Evaluate visualizations for common pitfalls including violations of proportional ink, inappropriate use of color, misleading 3D effects, and poor data-to-context balance
- Design figures that handle overlapping data points, employ redundant coding for accessibility, and use appropriate labels, titles, and captions
- Critique data visualizations using established design principles and recommend specific improvements
- Construct a coherent visual narrative by selecting, designing, and sequencing multiple visualizations to tell a data-driven story
- Produce publication-quality, reproducible visualizations using appropriate software tools while considering format requirements and audience needs
- Develop and present a complete data visualization project that demonstrates technical proficiency, design excellence, and effective communication of insights
Course Expectations
Attendance to the class is expected and necessary to complete the learning outcomes. Students may work together on assignments, but must submit any work as their own (no copying from other students or other sources).
For software-specific questions (Tableau, R, Python), please ask them on Ed Discussion first (so that anyone in the class can help). If Ed discussion does not address the question, please make use of the office hours.
Students may choose to use Tableau, R (with ggplot2), or Python (with matplotlib/seaborn/plotly/altair) for all course assignments. Thursday class sessions will include tutorials focused on Tableau, but concepts apply to all tools. Students using R or Python may use Large Language Models (LLMs) such as ChatGPT or Claude to assist with code generation, provided they understand and can explain all submitted code (see Academic Integrity section for details).
Prerequisites/Antirequisites
This course has no prerequisites and is accessible for students of all backgrounds. Any statistics or technical knowledge needed will be covered in class. General Education Code SR.
Required Materials, Textbooks, and Technology
Laptop or Desktop Computer
You will need a computer for homework, in-class activities, and lab work. You will not be able to complete most of the work on a mobile device. Students who need a laptop can make use of the library’s borrow program: https://library.ucsc.edu/services/computing/borrow-a-laptop
Textbook
Primary:
Wilke, Claus O. Fundamentals of Data Visualization. O’Reilly Media, 2019.
Available free online at https://clauswilke.com/dataviz/ (Print version optional)
Useful References:
- Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication. New Riders, 2016. UCSC library
- Wilkinson, Leland. The Grammar of Graphics. Springer, 2005. UCSC library
- OpenIntro Statistics: https://www.openintro.org/book/os/
Technology
- Personal computer with internet access
- Choose ONE of the following software options:
- Tableau Desktop (free student license) - recommended for beginners
- R with RStudio and ggplot2 - recommended for reproducible research
- Python with Jupyter and visualization libraries - recommended for data science workflows
- Canvas, GradeScope, and Google Suite access
- Ed Discussion account (free)
Assignments & Assessment
Class participation (9% total)
Measured using polls, and quick assignments (graded on completion) during every single class. This also includes completing the surveys, and syllabus acknowledgement.
Labs (40% total)
Software Labs (4 × 10% each = 40%): There will be 5 hands-on labs submitted on Canvas. Labs will be completed using your choice of Tableau, R, or Python. Labs due on the same day of the lab. Top 4 will be counted.
- Lab 1 (Week 1): Getting Started - installation party
- Lab 2 (Week 3): Aesthetics and mappings
- Lab 3 (Week 5): Amounts and Distributions
- Lab 4 (Week 7): Proportions and Hierarchies
- Lab 5 (Week 9): Time Series Analysis + Design Critique and Improvement
Concept Maps (6% total)
Concept Maps (2 × 3% each = 6%): There will be 3 concept maps to be submitted on Canvas. Concept maps synthesize key concepts and relationships. Top 2 will be counted. Due on Fridays: Weeks 2, 6, and 8.
- Concept Map 1 (Week 2): Data to Visualization - data types, aesthetics, scales, coordinates
- Concept Map 2 (Week 6): Chart Types - decision tree for visualization selection
- Concept Map 3 (Week 8): Design Principles - synthesizing Wilke Ch. 17-26
Term Project (45% total)
There will be one term project where you will work in pairs. You will identify a dataset and produce visualizations that address a concrete real-world question, demonstrating mastery of course principles. The project consists of the following components:
| Component | Weight | Due Date | Description |
|---|---|---|---|
| Project Proposal | 10% | Week 4 | Dataset description, 3-5 initial exploratory visualizations, 3 research questions, planned visualization types |
| Exploratory Data Analysis (EDA) | 10% | Week 10 | Complete exploratory analysis with 10-15 visualizations, documented patterns and findings |
| Presentation | 10% | Week 10 | 8-10 minute presentation of your project, includes peer feedback |
| Final Visual Report | 15% | Finals Week | 5-8 page report with 5-7 publication-quality visualizations, introduction, design justification, conclusion |
Total: 100 points = 9% (Participation) + 40% (Labs) + 6% (Concept Maps) + 45% (Project)
No late submissions allowed for any of the assessments.
Extra Credit
If more than 75% of the class completes the SETs, a 1% extra in the final grade will be added to all students. Furthermore, some of the assignments will include extra credit that can count towards the final grade.
Course Letter Grade
| Letter | Score | Letter | Score |
|---|---|---|---|
| A+ | x ≥ 99 | C+ | 70 ≤ x < 74.99 |
| A | 95 ≤ x < 98.99 | C | 65 ≤ x < 69.99 |
| A- | 90 ≤ x < 94.99 | C- | 60 ≤ x < 64.99 |
| B+ | 85 ≤ x < 89.99 | D | 50 ≤ x < 59.99 |
| B | 80 ≤ x < 84.99 | F | x < 50 |
| B- | 75 ≤ x < 79.99 | P/NP | x ≥ 65 / x < 65 |
Class Policies
- If you miss a class, you are responsible for staying updated with what happened; recordings will be available on Canvas.
- No extensions will be granted for final projects.
- If you cannot make it to all lectures, a policy of taking 85% of attendance as perfect attendance is in place, to provide flexibility in case of emergencies.
- In order to get an incomplete for the class, the student must have submitted at least 60% of the assignments by the end of the quarter.
Academic Integrity
All members of the UCSC community benefit from an environment of trust, honesty, fairness, respect, and responsibility. You are expected to present your own work and acknowledge the work of others in order to preserve the integrity of scholarship.
Academic integrity includes:
- Following project rules
- Incorporating proper citation of all sources of information
- Submitting your own original work
- Checking in as present only when you are actually present in lectures
Academic misconduct includes, but is not limited to, the following:
- Copying/purchasing any material from another student, or from another source, that is submitted for grading as your own
- Plagiarism, including the use of Internet material without proper citation
- Submitting your own work in one class that was completed for another class (self-plagiarism) without prior permission from the instructor
Use of Large Language Models (LLMs) for R/Python Code
Students using R or Python may use LLMs (ChatGPT, Claude, GitHub Copilot, etc.) to assist with code generation, BUT:
You MUST:
- Understand every line of code you submit
- Be able to explain what your code does and why
- Modify and customize generated code for your specific needs
- Test and verify that code produces correct results
- Cite LLM use in your code comments (e.g.,
# Generated with ChatGPT assistance, modified for X)
You CANNOT:
- Submit code you don’t understand
- Blame the LLM for errors in your assignment (“the AI made a mistake” is not an excuse)
- Use LLMs to generate concept maps or written explanations (these must be in your own words)
The Golden Rule:
You are responsible for understanding ALL code you submit. If you can’t explain what a line of code does when asked, you don’t understand it well enough to submit it.
Violations of the Academic Integrity policy can result in dismissal from the university and a permanent notation on a student’s transcript. For the full policy and disciplinary procedures on academic dishonesty, students and instructors should refer to the Academic Misconduct page at the Division of Undergraduate Education.
Student Hours for Class
This is a 5-unit course. It will require 3 hours of lecture, 5 hours of reading and practice, and 7 hours of homework and project work per week.
Instructor Feedback
The instructor and teaching team will provide direct comments and feedback on assignments through Canvas and GradeScope. Office hours are available for personalized feedback and guidance.
Student Feedback
At the end of the quarter, you will be asked to complete a Student Experience of Teaching survey for this course. SETs provide an opportunity for you to give valuable feedback on your learning that is honest and constructive. This anonymous feedback will help me consider modifications to the course that will help future students learn more effectively. Here’s more information about them: https://its.ucsc.edu/sets/index.html
Weekly Schedule
| Week | Topics | Textbook Chapters | Tutorial (Thursday)* | To-Do List |
|---|---|---|---|---|
| 1 | Intro + Aesthetics & Mapping Data | Ch 1-2 | Install + Interface Basics | Syllabus Acknowledgment Initial Survey Lab 1 |
| 2 | Color & Coordinate Systems | Ch 3-4 | Color Palettes & Scales | Concept Map 1 |
| 3 | Visualizing Amounts & Distributions | Ch 5-7 | Histograms, Bars, Heatmaps | Lab 2 |
| 4 | Multiple Distributions & Comparisons | Ch 8-9 | Box Plots & Small Multiples | Project Proposal |
| 5 | Proportions & Nested Data | Ch 10-11 | Hierarchy & Drill-down, Treemaps | Lab 3 |
| 6 | Associations & Relationships | Ch 12 | Scatter Plots & Trend Lines | Concept Map 2 |
| 7 | Time Series & Trends | Ch 13-14 | Calculated Fields, Parameters & Sets | Lab 4 |
| 8 | Geospatial & Uncertainty | Ch 15-16 | Maps & Error Bars | Concept Map 3 |
| 9 | Design Principles & Storytelling | Ch 17-29 (selections) | Dashboards & Stories | Lab 5 |
| 10 | Project Presentations | — | Final Polish & Export | EDA + Project Presentation |
| 11 | Finals Week | — | — | Final Project Submission |
* Thursday tutorials focus on Tableau, but concepts apply to all tools (R, Python). Students using R/Python can follow along conceptually and implement in their chosen tool.
Tips to Succeed in This Class
- Read the assigned textbook chapters each week (available free online)
- Attend class and be an active participant
- Read the syllabus and understand the evaluation of the class
- Start assignments early - visualization requires iteration and refinement
- Work on labs weekly; this will make preparing for your final project very easy
- Find a study group and commit to it - learning is always better with a learning community
- Work on your concept maps progressively throughout the relevant weeks
- Practice with your chosen software regularly (Tableau, R, or Python)
- Don’t leave questions about the material unanswered - use office hours and Ed Discussion
- Critically evaluate visualizations you encounter in daily life (news, social media, reports)
- Build a portfolio of your work throughout the quarter
Accessibility
UC Santa Cruz is committed to creating an academic environment that supports its diverse student body. If you are a student with a disability who requires accommodations to achieve equal access in this course, please submit your Accommodation Authorization Letter from the Disability Resource Center (DRC) to me privately during my office hours or by email, preferably within the first two weeks of the quarter. At this time, I would like us to discuss ways we can ensure your full participation in the course. I encourage all students who may benefit from learning more about DRC services to contact the DRC by phone at 831-459-2089 or by email at drc@ucsc.edu.
Religious Accommodation
UC Santa Cruz welcomes diversity of religious beliefs and practices, recognizing the contributions differing experiences and viewpoints can bring to the community. There may be times when an academic requirement conflicts with religious observances and practices. If that happens, students may request reasonable accommodation for religious practices. The instructor will review the situation in an effort to provide a reasonable accommodation without penalty. You should first discuss the conflict and your requested accommodation with your instructor early in the term. You or your instructor may also seek assistance from the Dean of Students office.
Principles of Community
The University of California, Santa Cruz expressly prohibits students from engaging in conduct constituting unlawful discrimination, harassment or bias… More here. I am committed to providing an atmosphere for learning that respects diversity and supports inclusivity. We need to work together to build this community of learning. I ask all members of this class to:
- be open to and interested in the views of others
- consider the possibility that your views may change over the course of the term
- be aware that this course asks you to reconsider some “common sense” notions you may hold
- honor the unique life experiences of your colleagues
- appreciate the opportunity that we have to learn from each other
- listen to each other’s opinions and communicate in a respectful manner
- keep confidential discussions that the community has of a personal (or professional) nature
- ground your comments in the course materials. Refer frequently to the textbook and examples, and make them the focus of your questions, comments, and arguments. This is the single most effective way to ensure respectful discussion and to create a space where we are all learning together.
Title IX/CARE Advisory
The Title IX Office is committed to fostering a campus climate in which members of our community are protected from all forms of sex discrimination, including sexual harassment, sexual violence, and gender-based harassment and discrimination. Title IX is a neutral office committed to safety, fairness, trauma-informed practices, and due process.
Title IX prohibits gender discrimination, including sexual harassment, domestic and dating violence, sexual assault, and stalking. If you have experienced sexual harassment or sexual violence, you can receive confidential support and advocacy at the Campus Advocacy Resources & Education (CARE) Office by calling (831) 502-2273. In addition, Counseling & Psychological Services (CAPS) can provide confidential, counseling support, (831) 459-2628. You can also report gender discrimination directly to the University’s Title IX Office, (831) 459-2462. Reports to law enforcement can be made to UCPD, (831) 459-2231 ext. 1. For emergencies call 911.
Student Services
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Slug Support Program
College can be a challenging time for students and during times of stress it is not always easy to find the help you need. Slug Support can give help with everything from basic needs (housing, food, or financial insecurity) to getting the technology you need during remote instruction. To get started with SLUG Support, please contact the Dean of Students Office at 831-459-4446 or you may send us an email at deanofstudents@ucsc.edu.
Slug Help/Technology
The ITS Support Center is your single point of contact for all issues, problems or questions related to technology services and computing at UC Santa Cruz. To get technological help, simply email help@ucsc.edu.