Learn how to apply colour theory to improve your Data Science & Analytics data visualisations and presentations

Colors for Data Science A-Z: Data Visualization Color Theory

Name: Color theory fundamentals and tool-based visualization
Rating: 7.1

InstructorPatrycja Hannagan

Duration3h 47m

Students7,961

Rating4.7 (1,448)

Color theory fundamentals and tool-based visualization

Strengths

Technical Color Fundamentals

Covers essential concepts such as tints, shades, saturation, and the distinction between RGB and CMYK.

Practical Tool Integration

Provides instruction on using industry-standard tools like Adobe Color, Paletton, and ColorBrewer for palette creation.

Applied Visualization Walkthrough

Includes a project-led segment using Tableau Public to demonstrate color application on real data examples.

Limitations

Manual Selection Focus

Instruction centers on manual color selection and lacks coverage of automated or programmatic methods.

Best suited to

Aspiring data presenters
Beginners seeking design fundamentals
Analysts using Tableau Public

Less suited to

Data scientists requiring automated color workflows
Advanced users looking for programmatic palette generation

Comidoc Score

7.1/10

Recommended

Beginner-friendly

Comidoc verdict

Technical mechanics of color, such as saturation and achromatic properties, form the core of the instruction. This foundation is paired with practical tool usage for generating palettes and a project walkthrough using Tableau Public to ground these theories in data visualization.

Instruction focuses on manual selection processes rather than automated or programmatic methods. One learner signal notes the absence of ways to automate color choices through code or advanced workflows.

This profile suits those looking to build a fundamental understanding of color theory to improve their presentation skills.

Score breakdown