J677: Concepts and Tools for Data Analysis and Visualization
Fall 2025University 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 5145
Course Staff
Instructor
Ross Dahlke, PhD
ross.dahlke@wisc.edu
Office: 5166 Vilas Hall
Office Hours: Tuesday 12:00–1:00 PM
Teaching Assistant
Wil M. Dubree, MA
dubree@wisc.edu
Office: 5165 Vilas Hall
Office Hours: TBD or by appointment
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: September 1–5, 2025
Thursday: Lecture - Syllabus and Intro to Data Visualization
Week 2: September 8–12, 2025
Tuesday: Lecture - Intro to R, RStudio, Tidyverse, & Data Structures
Thursday: Lecture - More R & Tidyverse
Week 3: September 15–19, 2025
Tuesday: Lecture - Intro to ggplot & Univariate Visualization
Thursday: Lecture - Bivariate Visualization: Bar Plots
Week 4: September 22–26, 2025
Tuesday: Lecture - Bivariate Visualization: Scatter Plots
Thursday: Lecture - Data Sources
Week 5: September 29–October 3, 2025
Tuesday: Final Project - Exploratory Data Analysis (Individual)
Thursday: Lecture - Data Cleaning
Week 6: October 6–10, 2025
Tuesday: Group Assignment - The Best and Worst of Data Visualization
Thursday: Lecture - Themes, Facets, & Combining Graphs
Week 7: October 13–17, 2025
Tuesday: Final Project - Cleaned Dataset & Dictionary
Thursday: Lecture - Plot Axes
Readings:
Week 8: October 20–24, 2025
Tuesday: Group Assignment - Data Visualization Recreation
Thursday: Lecture - Color, Color Theory, & Accessibility
Week 9: October 27–31, 2025
Tuesday: Final Project - Instagram Post
Thursday: Lecture - Visualizing Uncertainty
Week 10: November 3–7, 2025
Tuesday: Group Assignment - AI Client Simulation
Thursday: Lecture - Visual Focus
Week 11: November 10–14, 2025
Tuesday: Final Project - Infographic
Thursday: Lecture - Annotations, Legends, & Guides
Week 12: November 17–21, 2025
Tuesday: Final Project - AI Role Playing
Thursday: Lab - Writing Center (Resume)
Week 13: November 24–28, 2025
Tuesday: Lab - Chazen Museum Visit
Thursday: No Class - Thanksgiving Break
Week 14: December 1–5, 2025
Tuesday: Final Project - Poster Peer Feedback Session
Thursday: Final Project - Instructor Feedback Session
Week 15: December 8–12, 2025
Tuesday: 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)