which coursekata textbook is right for me?

Books for High School

CourseKata offers four versions of its Statistics and Data Science textbook, each designed to serve different teaching contexts and pacing needs. 

  • Statistics & Data Science I (AB)
  • Statistics & Data Science II (XCD)
  • Advanced Statistics and Data Science I (ABC)
  • Algebra + Data Science (G)

All of our high school textbooks share the same modern, modeling-first approach and introduce students to R as a tool for doing real data science. The key difference is how much content is included, what pacing is assumed, and the course context each version was designed to support.

These textbooks are each built from shared content blocks—A, B, C, D, X, G—each representing a few chapters of material. This guide explains what each content block means and how they combine into the books.

Understanding the Content Codes: A, B, C, D, and X

Each content code represents multiple chapters of the textbook. These codes help describe which material is included in each version of the book. 

A: Exploring Variation

4 chapters

  • Introduces students to R, data, and visualizations of distributions and relationships.
  • Topics include: measurement, sampling, tidy data, data visualizations (e.g., histograms, box plots, scatter plots, bar graphs, contingency tables), research methods, descriptive statistics

B: Modeling Variation

5 chapters

  • Builds foundational understanding of statistical models with a single predictor (categorical or quantitative).
  • Topics include: General Linear Model (GLM), model predictions, error (residuals, sums of squares), ANOVA, simple regression, correlation, proportion reduction in error (PRE), F-statistic, degrees of freedom 

C: Evaluating Models

3 chapters

  • Introduction to statistical inference rooted in computational methods (e.g., simulation, randomization, and bootstrapping)
  • Topics include: sampling distributions, t-test, p-value, F-test, permutation test, confidence intervals

D: Multivariate Models

4 chapters

  • Introduces models with multiple predictors, categorical and quantitative.
  • Topics include: multiple regression, factorial ANOVA, ANCOVA, main effects and interactions, partial correlation

X: Accelerated A + B

3 chapters

  • Covers the same core ideas from A and B but in a compressed format for faster-paced or advanced courses.

G: Data Science for Algebra

3 chapters

  • Introduces algebra students to R and using algebraic functions to model data
  • Topics include: tidy data, scatter plots, algebraic functions, programming functions, linear functions, fitting models, model predictions, error (e.g., residuals, RMSE, PRE)

Which Book Is Right for You?

Here’s how the codes combine into each of our four textbooks. Note that no prior experience with R assumed for any of the books.

Statistics & Data Science I (AB)

  • Best for: Full-year courses or one semester course that focus on exploring and modeling variation; a great introduction to R and using functions to model interesting data

Statistics & Data Science II (XCD)

  • Best for: Follow-up course or second-semester offerings for students who have completed AB

Advanced Statistics and Data Science I (ABC)

  • Best for: Full-year introductory statistics and data science courses focused on building a strong foundation in modeling, data visualization, and statistical inference

Algebra + Data Science (G)

  • Best for: Algebra or Integrated Math courses that want to motivate algebraic functions by showing how they are used to model real-world data; designed as supplemental units to introduce students to data science thinking without assuming prior coding or statistics knowledge.

Still not sure? Feel free to [contact us] or preview the textbooks [link].

Additional Resources

[Download a detailed list of course goals and learning objectives (PDF)]

[Download a mapping of traditional statistics topics and where they are covered in CourseKata (PDF)]

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