https://orcid.org/0000-0002-7930-6701
An Introduction to R and Python For Data Analysis is a welcome new educational resource, designed for graduate students, newcomers to programming, and those in the field of data science and statistics. Its dual-language approach, offering side-by-side instruction in both R and Python, sets it apart in the literature. Unlike traditional texts that focus on a single language or sequentially present multiple languages, this book introduces R and Python simultaneously, catering to an audience that requires practical knowledge in both. The book is nicely structured, beginning with fundamental programming concepts in R and Python and progressively introducing more complex topics. This progression is beneficial for readers with no previous computing experience, as it starts with the basics, including installing R (RStudio) and Python (Anaconda), complete with helpful screenshots. The clear and concise presentation of each programming language, accompanied by in-text explanations and highlighted key concepts, makes the learning process accessible.
This book includes exercises and real-world examples, which I think enhances the learning experience as it provides hands-on practice. These exercises are designed with automatic grading in mind, which is a very nice feature. This approach is particularly advantageous for instructors, as the book has been successfully “battle-tested” in real classroom settings. The book introduces the essentials of R and Python, without going in too deep into advanced programming or statistical analysis. This choice allows for a broader overview of each language, though it might result in less exhaustive coverage compared to texts dedicated to a single language. The author’s decision to not overly focus on certain aspects, like visualization tools, acknowledges the vast amount of existing materials from various online resources.
An Introduction to R and Python For Data Analysis provides a balanced approach to teaching both R and Python, a requirement in many data analysis roles today. This book is ideally suited as a course text at either the undergraduate or the graduate level and is a nice choice for instructors. It can be used for self-study or as a comprehensive guide for a full course. Its integration with a GitHub repository further enhances its practicality.
In conclusion, this book stands out for its innovative dual-language instruction, practical approach, and accessibility to beginners. While it might not cover advanced topics extensively, it serves as an excellent foundational resource that could be complemented with other literature focused on statistical modeling. This book is a recommended choice for educators and students, who seek a comprehensive introduction to two of the most important programming languages in contemporary data analysis.
Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
[email protected]