J677: Concepts and Tools for Data Analysis and Visualization
Spring 2026University of Wisconsin-Madison School of Journalism and Mass Communication
Like no other time, our world is recorded in digital formats through social networks, online news platforms, mobile devices, and more. This constant flow of information has given rise to new possibilities for understanding social phenomena, communicating insights, and driving data-informed decisions in fields like journalism, strategic communication, and beyond.
Course Logistics
Schedule
Tuesday & Thursday 1:00–2:15 PM
Location
Vilas 5055
Course Staff
Instructor
Ross Dahlke, PhD
ross.dahlke@wisc.edu
Office: 5166 Vilas Hall
Office Hours: Tuesday & Thursday 12:15–1:00 PM
Teaching Assistant
Wil M. Dubree, MA
dubree@wisc.edu
Office: 5165 Vilas Hall
Office Hours: Monday 10:00–12:00 PM
Course Objectives
- •Identify and address the practical, ethical, and inclusive challenges of data collection, management, analysis, and presentation, ensuring responsible use and communication of digital media data.
- •Demonstrate a solid understanding of the grammar and principles of data visualization, applying them to create clear, engaging, and contextually relevant data narratives for diverse audiences.
- •Attain proficiency with industry-relevant tools, including R, tidyverse, and generative AI, to effectively prepare, explore, and visualize data in real-world media and communication settings.
- •Develop the capacity to handle and visualize diverse data types, integrating these skills into compelling, data-driven storytelling projects.
Course Schedule
Week 1: January 19–23, 2026
Tuesday: Lecture - Syllabus and Intro to Data Visualization
Thursday: Lecture - Intro to R, RStudio, Tidyverse, & Data Structures
Week 2: January 26–30, 2026
Tuesday: Lecture - More R & Tidyverse
Thursday: Lecture - Intro to ggplot & Univariate Visualization
Week 3: February 2–6, 2026
Tuesday: Lecture - Bivariate Visualization: Bar Plots
Thursday: Lecture - Bivariate Visualization: Scatter Plots
Week 4: February 9–13, 2026
Tuesday: Lecture - Data Sources
Thursday: Final Project - Exploratory Data Analysis (Individual)
Week 5: February 16–20, 2026
Tuesday: Lecture - Data Cleaning
Thursday: Group Assignment - The Best and Worst of Data Visualization
Week 6: February 23–27, 2026
Tuesday: Lecture - Color, Color Theory, & Accessibility
Thursday: Final Project - Cleaned Dataset & Dictionary
Week 7: March 2–6, 2026
Tuesday: Lecture - Themes, Facets, & Combining Graphs
Thursday: Group Assignment - Data Visualization Recreation
Week 8: March 9–13, 2026
Tuesday: Lecture - Plot Axes
Readings:
Thursday: Final Project - Instagram Post
Week 9: March 16–20, 2026
Tuesday: Lecture - Visualizing Uncertainty
Thursday: Group Assignment - AI Client Simulation
Week 10: March 23–27, 2026
Tuesday: Lecture - Visual Focus
Thursday: Lab - Chazen Museum Visit
Week 11: April 6–10, 2026
Tuesday: Final Project - Infographic
Thursday: Lecture - Annotations, Legends, & Guides
Week 12: April 13–17, 2026
Tuesday: Final Project - AI Role Playing
Thursday: Lab - Writing Center (Resume)
Week 13: April 20–24, 2026
Tuesday: Final Project - Poster Working Session
Thursday: Final Project - Poster Peer Feedback Session
Week 14: April 27–May 1, 2026
Tuesday: Final Project - Instructor Feedback Session
Thursday: Final Project - Poster Presentations
Required Textbooks
R Graphics Cookbook: Practical Recipes for Visualizing Data, 2nd Edition
Chang, W.
O'Reilly Media (2018)
Data Visualization: A Practical Introduction
Healy, K.
Princeton University Press (2018)
R for Data Science, 2nd Edition
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G.
O'Reilly Media (2023)
Recommended Reading
Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations
Berinato, S.
Harvard Business Press (2016)
The Functional Art
Cairo, A.
New Riders (2012)
How Charts Lie: Getting Smarter About Visual Information
Cairo, A.
W.W. Norton & Company (2019)