Books
CourseKata’s library of content for teaching statistics and data science includes 6 different interactive online textbooks for colleges and high schools. How do you know which book to use? First, you need to get a sense of the different types of CourseKata books that the instructors implement. Then, you’ll need information on the content codes.
Overview of CourseKata Books
Books for College
The CourseKata Statistics and Data Science college textbook is being used at numerous colleges and universities across a wide range of academic disciplines, including Mathematics, Statistics, Psychology, Sociology, and Political Science.
Currently, CourseKata offers three versions of the textbook for college:
Introductory Statistics with R (ABC) includes CourseKata’s gentle introduction to R, data visualization and descriptive statistics, basics of statistical modeling (e.g., one-way ANOVA and simple regression), and an approach to inferential statistics grounded in simulation, randomization, and bootstrapping.
Advanced Statistics with R (ABCD) includes all of the above, plus additional chapters on multivariate models.
- Accelerated Statistics with R (XCD)
Books for High School
The CourseKata Statistics and Data Science high school textbooks have the same content as the college textbooks but are paced differently. Colleges often teach this material in one semester or quarter while high schools may take 1-2 years to cover the same material.
Explanation of Content Codes
The key to deciding which book is appropriate for your students is to understand the content codes. The content represented by each code is covered in 3-4 chapters of the textbook. The same content on CourseKata may appear in multiple books. You need to decide which is the right mix and pacing for your students.
| Code | Content | Overview | Topics |
|---|---|---|---|
| A | Exploring Variation | Introduction to R, data frames and visualization of distributions and relationships | Measurement, histograms, box blots, scatter plots, contingency tables, descriptive statistics |
| B | Modeling Variation | Introduction to modeling, and building models with single predictors (simple ANOVA and regression) | ANOVA, regression, correlation, model predictions, sum of squares |
| C | Evaluating Variation | Introduction to statistical inference with a focus on simulation, randomization and bootstrapping | t-test, p-value, F, R-squared, randomization/permutation test bootstrapping |
| D | Multivariate Models | Building and interpreting models with multiple predictors that are categorical, quantitative, or both | multiple regression, ANCOVA partial correlation |
| X | Accelerated A + B | An accelerated version of A + B for advanced students or students with prior experience | (see A + B) |
Download a detailed list of course goals and learning objectives .
Download a mapping of traditional statistics topics and where they are covered in CourseKata.