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

Explore color theory with a pirate-map themed journey, covering the color wheel basics, achromatic colors, and RGB versus additive and subtractive systems for data visualization.

Explore what color is by linking light waves, wavelength and frequency to visible light, the spectrum, and how our brain interprets electromagnetic signals as color.

Master the color wheel and essential terms for data visualization. Learn primary, secondary, and tertiary colors, their mixing, and the 12 basic hues that define color theory.

Master tint, shade, and tone on the color wheel to control saturation and brightness. Use these terms to select effective colors for data visualization.

Explore achromatic colors, black, white, and gray, as hues without color, why they sit outside the color wheel, and how shades and tints use them in color theory.

Compare the subtractive CMYK color system used in printing with the additive RGB system used on screens, and learn why colors differ between prints and displays.

Explore color blindness in data visualization and color theory, and design accessible visuals with patterns, labels, and colorblind palettes.

Learn how color psychology shapes emotions, behavior, and decisions, as brands use hues to influence responses. Apply insights to data science visuals by selecting colors that evoke alertness or calm.

Explore how color meanings vary across cultures, religion, and memories, and how blue evokes tranquility and security in data visualization and branding, with examples like weddings, funerals, and Facebook.

Explore how warm colors evoke heat and mood from sunsets, fire, and deserts, while cool colors convey refreshing, windy landscapes and calm, using the color wheel and art examples.

Explore yellow, orange, and red as warm colors and their meanings for data visualization. Yellow draws attention and signals optimism, caution; orange invites action; red conveys power, passion, and urgency.

Identify how green signals freshness, nature, and money within the color wheel, while blue conveys trust and productivity and purple evokes luxury and creativity.

Explore three free online tools for data visualization color theory: Adobe Color, Peloton, and Colorbrewer 2.0, learn to pick color schemes and palettes for compelling data visuals.

Explore Adobe color’s color wheel and predefined schemes to create palettes, maintain chosen color scheme as you fine-tune, copy hex or RGB codes, or extract colors from images for Tableau.

Discover Peloton, a powerful color palette tool for data visualization, offering monochromatic, analogous, complementary, split-complementary, triad, and tetrad schemes, with randomize, fine-tune, and shift-to-adjust features.

Learn Colorbrewer 2.0 for quick data science color palettes, choosing number of classes and applying sequential, diverging, or single-hue schemes with colorblind-safe and print-friendly options.

Explore how color context shapes perception and color harmony in data visualization, learning to plan color schemes with analogous and complementary palettes to make elements stand out without overstimulation.

Explore how color schemes govern data visualization, from monochromatic to tetradic, using the color wheel to map hues and tones and apply analogous, complementary, split-complement, and triadic schemes for contrast.

Explore monochromatic color schemes from a single hue using real data visuals, focusing on blue palettes that convey water, and learn to generate them with dope color, peloton, and colorbrewer.

Analyze analogous color schemes using real data examples, selecting hues close on the color wheel, building a five-color palette, and applying cohesive palettes to charts like pie graphs.

Explore complementary and split-complementary color schemes with real data examples, showing high contrast and how to apply them in charts using orange and blue, purple and yellow, and Colorbrewer tools.

Master triadic color schemes with three equally important colors and explore tetradic palettes of four colors, including balancing brightness for clear data visualizations.

Choose data visualization backgrounds that maximize readability and visual impact by balancing contrast, white or black backgrounds, monochromatic palettes, and applying subtle tints, shapes, and background images thoughtfully.

analyze a vitamin trials dataset to visualize dose effects on cognitive test scores using a single, uncluttered scatter plot, applying colors for data science to reveal group and dose patterns.

Download and install Tableau Public, the free drag-and-drop data visualization tool. Explore the gallery and public profiles as you prepare datasets for coloring in data science.

Create a scatterplot in Tableau Public by connecting a CSV dataset and mapping vitamin dose to test score. Use color by group to reveal trends.

Test and compare color palettes for data visualizations using color theory, analyze how contrast affects the visibility of trends, and implement effective color schemes that make trends stand out.

Apply a split complementary color scheme to data visualizations using the color wheel and Peloton, highlighting the children category while keeping men and women similar.

Color the subcategories using a split complementary color scheme to create distinct, cohesive palettes. Adjust tints, shades, and tones in tools like Tableau to highlight trends and ensure readability.

Apply a triad color scheme to create balanced visuals that reveal age-group trends in vitamin dosage. De-emphasize noncritical categories like adults and use Peloton tools to present clear, comparable data.

Apply an analogous color scheme to convey sequential progression while using equidistant colors to avoid bias, and compare with triad and adjacent schemes for data visualization.

Explore how color schemes like split complementary, monochromatic, triad, and analogous reveal trends, with tints and outlines to highlight outliers.