This book teaches data analysis and software development in R, covering techniques for data manipulation, visualization, and building software packages. It is ideal for beginners with data science or analytics background and covers computational methods and programming in R.

Format: Paperback / softback
Length: 511 pages
Publication date: 24 June 2022
Publisher: APress

Discover Best Practices for Data Analysis and Software Development in R and Start on the Path to Becoming a Fully-Fledged Data Scientist

Updated for the R 4.0 release, this comprehensive book teaches you techniques for both data manipulation and visualization, and guides you on the path to becoming a fully-fledged data scientist.

Beginning Data Science in R 4, Second Edition delves into the realm of data science, which is a fusion of statistics, computational science, and machine learning. You'll explore how to effectively structure and mine data to extract valuable patterns and construct mathematical models. This process necessitates computational methods and programming, and R emerges as an ideal programming language for this purpose.

Modern data analysis demands computational skills and often involves a minimum of programming. By reading and applying the knowledge presented in this book, you'll gain the essential skills to embark on your journey with R programming, specifically for data science applications.

Furthermore, comprehensive source code will be available to support your future projects. You can access the source code at github.com/Apress/beg-data-science-r4.

Who This Book Is For:

This book is designed for individuals with a background in data science or analytics, but who may not have prior experience with the R programming language. Whether you're a researcher, analyst, or business professional seeking to leverage the power of R for data-driven insights, this book will be your invaluable guide.

By mastering the techniques outlined in this book, you'll be well-equipped to perform data science and analytics using statistics and the R programming language. You'll gain proficiency in visualizing and exploring data, even working with large data sets commonly encountered in big data scenarios. You'll also learn how to build your own R packages, ensuring the reusability and maintainability of your code.

Furthermore, you'll delve into essential practices such as testing and checking your code, practicing version control, and optimizing your code for performance. This comprehensive coverage will empower you to develop robust and efficient data science solutions using R.

So, whether you're a beginner or an experienced programmer looking to expand your skills in data science, this book is your ultimate resource. Take the first step towards becoming a data scientist and unlock the full potential of R programming with Beginning Data Science in R 4, Second Edition.

Weight: 1016g
Dimension: 254 x 178 (mm)
ISBN-13: 9781484281543
Edition number: 2nd ed.