INFO 3402

Information Exposition

INFO 3402 “Information Exposition” is a semester-length, lecture-based undergraduate course. This course teaches students how to communicate the findings from their data analyses and to understand the ethics and implications of data communication and storytelling. Students will develop their computational skills for reshaping, analyzing, summarizing, and visualizing quantitative data using Python’s scientific libraries. Students will also develop their communication skills for interpreting, designing, and storytelling the findings of their analyses for general and professional audiences.

Learning objectives

  • Analyze and interpret relationships in quantitative data
  • Create effective data visualizations through iterative design
  • Communicate findings for general and professional audiences
  • Think critically about and critique data narratives and visualizations


Module Week Theme Type Skills
Shaping 1 Loading Computation Reading from file and web
  2 Aggregating Computation Pivot tables, groupby-aggregation
  3 Combining Computation Concatenation and joins
  4 Tidying Computation Wide vs. tidy, melting
Distribution 5 Histograms Computation Counts, cuts, transformations
  6 Audience Communication Genres and credibility
Comparison 7 Cat plots Computation Multiple plots, hues, facets
  8 Persuasion Communication Influence and biases
Trend 9 Time series Computation Temporal data, resampling, forecasting
  10 Uncertainty Communication Variation and uncertainty
Spring Break 11     No class!
Relationship 12 Scatter plot Computation Trends, correlations, linear models
  13 Validity Communication Types, causality, counterfactuals
Text 14 Processing Computation Tokens, frequencies, sentiment
  15 Storytelling Communication Genre and narrative


  • Data Cleaning and Analysis
    • Chen, D. (2018). Pandas for Everyone: Python Data Analysis.
    • Elgabry, O. (2019). “The Ultimate Guide to Data Cleaning.” Towards Data Science.
    • Harrison, M. & Petrou, T. (2020). Pandas 1.x Cookbook: Practical Recipes.
    • Heydt, M. (2017). Learning Pandas: High-Performance Data Manipulation and Analysis in Python.
    • Klosterman, S. (2019). Data Science Projects with Python.
    • McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython.
    • Mertz, D. (2021). Cleaning Data for Effective Data Science.
    • Molin, S. & Jee, K. (2021). Hands-On Data Analysis with Pandas.
    • Nelli, F. (2018). Python Data Analytics: With Pandas, NumPy, and Matplotlib.
    • Osborne, J. (2013). Best Practices in Data Cleaning.
    • Stepanek, H. (2020). Thinking in Pandas: How to use the Python Data Analysis Library the Right Way.
    • Vanderplas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data.
    • Walker, M. (2020). Python Data Cleaning Cookbook.
    • Wickham, H. (2014). “Tidy Data.” J. Statistical Software.
  • Data Visualization
    • Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.
    • Döbler, M. & Grössmann, T. (2019). Data Visualization with Python.
    • Engebretsen, M. (2020). Data Visualization in Society.
    • Holtz, Y. (2021). Python Graph Gallery.
    • Jones, B. (2014). Communicating Data with Tableau: Designing, Developing, & Delivering Data Visualizations.
    • Keller, B. (2018). Mastering Matplotlib 2.x: Effective Data Visualization Techniques with Python.
    • Kirk, A. (2012). Data Visualization: A Successful Design Process.
    • Lima, M. (2011). Visual Complexity: Mapping Patterns of Information.
    • Matplotlib. (2021). Tutorials, Examples, and Twitter.
    • Meyer, M. & Fisher, D. (2018). Making Data Visual: A Practical Guide to Using Visualization for Insight.
    • Milovanović, I., Vettigli, G., & Foures, D. (2015). Python Data Visualization Cookbook.
    • Munzner, T. & Maguire, E. (2015). Visualization Analysis & Design.
    • Pajankar, A. (2021). Hands-On Matplotlib: Learning Plotting and Visualizations with Python 3.6
    • Schwabish, J. (2014). “An Economist’s Guide to Visualizing Data.” J. Economic Perspectives.
    • Schwabish, J. (2021). Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks.
    • Sosulski, K. (2019). Data Visualization Made Simple: Insights into Becoming Visual.
    • Tufte, E. (2001). The Visual Display of Quantitative Information.
    • Wilke, C. (2019). Fundamentals of Data Visualization.
    • Wilkinson, L. & Wills, G. (2005). The Grammar of Graphics.
    • Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics.
    • Yim, A., Chung, C., & Yu. A. (2018). Matplotlib for Python Developers.
  • Data Communication and Storytelling
    • Abela, A. (2013). Advanced Presentations by Design: Creating Communication That Drives Action.
    • Allchin, C. (2021). Communicating with Data: Making Your Case with Data.
    • Andrews, R. (2019). Info We Trust: How to Inspire the World with Data.
    • Bach, B., et al. (2018). “Design Patterns for Data Comics.” CHI.
    • Blount, T., et al. (2020). “Use of Narrative Patterns by Novice Data Storytellers.” CHIRA.
    • Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication.
    • Cairo, A. (2019). How Charts Lie: Getting Smarter about Visual Information.
    • Data Comics for Visual Storytelling.
    • Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals.
    • Gemignani, Z. (2014). Data Fluency: Empowering Your Organization with Effective Data Communication.
    • Gershon, N. & Page, W. (2001). “What Storytelling Can Do for Information Visualization.” CACM.
    • Jones, B. (2020). Avoiding Data Pitfalls.
    • Kahan, D., Jamieson, K. & Scheufele, D. (2017). Handbook on the Science of Science Communication.
    • Knaflic, C. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals.
    • Kriebel, A. & Murray, E. (2018). #MakeoverMonday: Improving How We Visualize and Analyze Data.
    • Matthews, R. & Wacker, W. (2007). What’s your story?: Storytelling to move markets.
    • Narrative Patterns for Data-Driven Storytelling.
    • Nolan, D. & Stoudt, S. (2021). Communicating with Data: The Art of Writing for Data Science.
    • Ojo, A. & Heravi, B. (2017). “Patterns in AwardWinning Data Storytelling.” Digital Journalism.
    • Riche, N., Hunter, C., Diakopoulos, N., & Carpendale, S. (2018). Data-Driven Storytelling.
    • Segel, E. & Heer, J. (2010). “Narrative Visualization: Telling Stories with Data.” TVCG.
    • Steele, J. & Iliinsky, N. (2010). Beautiful Visualization: Looking at Data Through the Eyes of Experts.
    • Swires-Hennessy, E. (2014). Presenting Data: How to Communicate Your Message Effectively.
    • Vora, S. (2019). The Power of Data Storytelling.
    • Wexler, S. (2021). The Big Picture: How to Use Data Visualization to Make Better Decisions—Faster.
    • Yang, L., et al. (2021). “Applying the Freytag’s Pyramid Structure to Data Stories.” TVCG.
    • Yau, N. (2013). Data Points: Visualization that Means Something.
  • Data Journalism and Newsletters

    Course materials