Data visualization
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing calculator
Choose materials, tutoring, or both — or book a single session as needed. Customize your plan on the subscribe page.
Billed in 15-minute increments (15-minute minimum, up to 4 hours). No subscription required.
$60.00 · 60 min · Undergraduate · Online ($60/hr)
Book through intake or schedule a session.
Topics typically covered
Standard syllabus
Visual perception and design
- Principles of effective data visualization
- Color, contrast, and accessibility
- Chart junk and misleading axes
- Choosing chart types for data types
- Small multiples and faceting
Grammar of graphics
- ggplot2 layered grammar in R (or equivalent)
- Mapping aesthetics to variables
- Scales, coordinates, and themes
- Interactive visualization (plotly, Shiny intro)
- Geospatial visualization basics
Communication
- Storytelling with data
- Dashboard design principles
- Visualization for exploratory vs explanatory analysis
- Critique and revision of published graphics
STEM / applied
Applied visualization projects
- Portfolio projects with real datasets
- Animation and transitions for time-based data
- Visualization in Python (matplotlib, seaborn, altair)
- Publishing static and interactive figures on the web
- Reproducible figure pipelines for reports
- Data journalism and policy visualization case studies
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Often cross-listed with data science programs. Emphasizes grammar of graphics and honest visual communication.