STEM / applied
Data visualization · Undergraduate · Math
Learning objectives from the Data visualization syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Data visualization — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Visual perception and design
- Principles of effective data visualization — Principles of effective data visualization
- Color, contrast, and accessibility — Color, contrast, and accessibility
- Chart junk and misleading axes — Chart junk and misleading axes
- Choosing chart types for data types — Choosing chart types for data types
- Small multiples and faceting — Small multiples and faceting
Grammar of graphics
- ggplot2 layered grammar in R (or equivalent) — ggplot2 layered grammar in R (or equivalent)
- Mapping aesthetics to variables — Mapping aesthetics to variables
- Scales, coordinates, and themes — Scales, coordinates, and themes
- Interactive visualization (plotly, Shiny intro) — Interactive visualization (plotly, Shiny intro)
- Geospatial visualization basics — Geospatial visualization basics
Communication
- Storytelling with data — Storytelling with data
- Dashboard design principles — Dashboard design principles
- Visualization for exploratory vs explanatory analysis — Visualization for exploratory vs explanatory analysis
- Critique — Critique
- Revision of published graphics — Revision of published graphics
Learning objectives
Click an objective for study materials.
Visual perception and design
- Principles of effective data visualization — Principles of effective data visualization
- Color, contrast, and accessibility — Color, contrast, and accessibility
- Chart junk and misleading axes — Chart junk and misleading axes
- Choosing chart types for data types — Choosing chart types for data types
- Small multiples and faceting — Small multiples and faceting
Grammar of graphics
- ggplot2 layered grammar in R (or equivalent) — ggplot2 layered grammar in R (or equivalent)
- Mapping aesthetics to variables — Mapping aesthetics to variables
- Scales, coordinates, and themes — Scales, coordinates, and themes
- Interactive visualization (plotly, Shiny intro) — Interactive visualization (plotly, Shiny intro)
- Geospatial visualization basics — Geospatial visualization basics
Communication
- Storytelling with data — Storytelling with data
- Dashboard design principles — Dashboard design principles
- Visualization for exploratory vs explanatory analysis — Visualization for exploratory vs explanatory analysis
- Critique — Critique
- Revision of published graphics — Revision of published graphics
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Visual perception and design
Principles of effective data visualization
Coming soon - Grammar of graphics
ggplot2 layered grammar in R (or equivalent)
Coming soon - Communication
Storytelling with data
Coming soon
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$1,162 · Data visualization · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.