Practical Statistics for Data Scientists: 50+ Essential Concepts vs R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Overall winner: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Key Differences
R for Data Science (Mine Cetinkaya-Rundel) focuses on tidyverse and ggplot learning with clear writing and foundational library value, while Practical Statistics for Data Scientists (Peter Bruce et al.) provides a broader statistics catalogue with R and Python code and many more reviews. Choose R for Data Science if you want hands-on ggplot/tidyverse instruction and clearer exposition; choose Practical Statistics if you want a compact reference of statistical concepts in both R and Python
Practical Statistics for Data Scientists: 50+ Essential Concepts
A practical guide to core statistical concepts for data science with examples in R and Python. Readers find it accessible for starting data science statistics, though explanations and code quality receive mixed feedback
Pros
- clear focus on data science statistics
- includes both R and Python code
- practical concepts covered (50+)
Cons
- mixed explanation quality
- some Python code lacks comments
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Practical guide to using R for data import, tidying, transformation, visualization, and modeling. Key benefit: structured workflows for data science with emphasis on ggplot. Customer insight: valued for writing quality and library depth
Pros
- practical guide for data wrangling
- focus on tidyverse workflows
- emphasizes visualization with ggplot
- clear writing quality
Cons
- features: N/A
Head-to-Head
| Criteria | Winner |
|---|---|
| Price | Peter Bruce, Andrew Bruce, Peter Gedeck |
| Durability | Tie |
| Versatility | Mine Cetinkaya-Rundel |
| User Reviews | Mine Cetinkaya-Rundel |