Course Announcement: DataViz 101
DataViz 101 is an evidence-based introduction to the principles and practice of data visualization. The course integrates findings from perceptual psychology and design science to develop learners’ capacity to select, construct, and evaluate visual representations that are accurate, efficient, and ethically sound. Empirical research shows that certain graphical encodings support more precise judgments than others (Cleveland & McGill, 1984; Ware, 2021), while established design principles help reduce cognitive load and avoid distortion (Munzner, 2014; Tufte, 2001). The course emphasizes clarity, truthfulness, and audience-centered communication (Cairo, 2016; Few, 2012).
Learning outcomes
By the end of the course, learners will be able to:
- Diagnose analytical and communicative goals and match them to effective visual encodings and chart types (Cleveland & McGill, 1984; Munzner, 2014).
- Apply perception-informed design principles (e.g., preattentive features, color and form, Gestalt grouping) to improve interpretability (Ware, 2021).
- Construct clear bar, line, scatter, small-multiple, and distributional displays with appropriate scales, annotations, and uncertainty cues (Munzner, 2014; Few, 2012).
- Evaluate and revise visualizations to reduce clutter, avoid misleading transformations, and improve signal-to-noise ratio (Tufte, 2001).
- Design accessible and inclusive visuals, including color-vision-safe palettes and alternative encodings (Ware, 2021).
- Communicate analytical findings responsibly, including data provenance, limitations, and ethical considerations in representation (Cairo, 2016).
Who should enroll
- Analysts, researchers, policy professionals, and students seeking a rigorous foundations course.
- No prior coding experience is required. Basic familiarity with spreadsheets and descriptive statistics (e.g., mean, median, distributions) is recommended.
Course structure and assessments
- Six modules with weekly milestones:
- Foundations: tasks, encodings, and perception
- Comparative displays and distribution plots
- Color, typography, and layout for cognition and accessibility
- Time series, part-to-whole, and multivariate displays
- Mapping and spatial reasoning; choropleths and proportional symbols
- Narrative visualization, uncertainty, and ethical communication
- Assessments:
- Auto-graded knowledge checks to reinforce key concepts
- Applied labs producing reproducible visuals from real datasets
- Peer critique using structured rubrics grounded in research
- Capstone project: a concise analytical brief with a small set of publishable figures
Tools and resources
- Learners may choose one of the following toolpaths:
- Spreadsheet and Tableau Public
- Python (pandas, matplotlib, seaborn)
- R (ggplot2)
- An orientation module provides installation guides, datasets, and style templates.
- All datasets are curated for pedagogical clarity and ethical use; data sources and licenses are documented for citation in deliverables.
Academic integrity and citation
- Visualizations must include sources, units, and time coverage. When reusing designs or code, provide proper attribution. The course adopts APA 7th ed. for written components; figure notes follow discipline norms for data visualization.
Getting started
- On day 1, complete the orientation checklist, verify software setup, and attempt the readiness quiz.
- Early engagement in the discussion forum is encouraged to establish peer feedback channels for critique and revision.
References (APA 7th)
- Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. Berkeley, CA: New Riders.
- Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554.
- Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten (2nd ed.). Burlingame, CA: Analytics Press.
- Munzner, T. (2014). Visualization analysis and design. Boca Raton, FL: CRC Press.
- Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.
- Ware, C. (2021). Information visualization: Perception for design (4th ed.). Burlington, MA: Morgan Kaufmann.
We look forward to your participation in DataViz 101 and to supporting your development of rigorous, high-impact visual analysis and communication.
