J677: Concepts and Tools for Data Analysis and Visualization
Spring 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
Monday & Wednesday 2:30–3:45 PM
Location
Vilas 5145
Course Staff
Instructor
Ross Dahlke, PhD
ross.dahlke@wisc.edu
Office: 5166 Vilas Hall
Office Hours: Monday 3:45–4:45 PM
Teaching Assistant
Wil M. Dubree, MA
dubree@wisc.edu
Office: 5165 Vilas Hall
Office Hours: Wednesday 1:30–2:30 PM 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: January 22, 2025
Wednesday: Lecture - Syllabus and Intro to Data Visualization
Week 2: January 27–29, 2025
Monday: Lecture - Intro to R, RStudio, & Tidyverse
Wednesday: Lecture - Intro to Data & Data Structures
Week 3: February 3–5, 2025
Monday: Lecture - More R & Tidyverse
Wednesday: Lecture - Intro to ggplot & Univariate Visualization
Week 4: February 10–12, 2025
Monday: Lecture - Bivariate Analysis
Wednesday: Lecture - Bivariate Visualization
Week 5: February 17–19, 2025
Monday: Lecture - Data Sources
Wednesday: Final Project - Idea Pitch
Week 6: February 24–26, 2025
Monday: Lecture - Data Cleaning
Wednesday: Group Assignment - The Best and Worst of Data Visualization
Week 7: March 3–5, 2025
Monday: Lecture - Themes, Facets, & Combining Graphs
Wednesday: Final Project - Cleaned Dataset & Dictionary
Week 8: March 10–12, 2025
Monday: Lecture - Plot Axes
Readings:
Wednesday: Group Assignment - Tabular Data Visualization
Week 9: March 17–19, 2025
Monday: Lecture - Color, Color Theory, & Accessibility
Wednesday: Final Project - Instagram Post
Week 10: March 31–April 2, 2025
Monday: Lecture - Visualizing Uncertainty
Wednesday: Group Assignment - AI Client Simulation
Week 11: April 7–9, 2025
Monday: Lecture - Visual Focus
Wednesday: Final Project - Infographic
Week 12: April 14–16, 2025
Monday: Lecture - Annotations, Labels, Legends, & Guides
Wednesday: Final Project - Final Project AI Role Playing
Week 13: April 21–23, 2025
Monday: Final Project - Final Project Peer Feedback
Wednesday: Lecture - Writing Center (Resume)
Week 14: April 28–30, 2025
Monday: Final Project - Final Project Instructor Feedback
Wednesday: 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)