TOP 10 FREE ONLINE BOOKS TO LEARN R-CODE AND DATA SCIENCE Posted on : Oct 09 - 2020

By learning about R and Data science, humans are provided with ample of opportunities in the world of data.

The education about data science is not enough. The more we read and learn about data science, the more we become fascinated about the intricate learning data science has to offer. Since data science is the new hype and will continue to remain so in the future, here are top 10 free online books that are coherent and comprehensive to understand R/Data science.

1. Advanced R by Hadley Wickham- Aiming at the intermediate and advanced users, the book talks about the fundamentals of R and the data types, and solving wide range of programs using functional programming. This book is a must go if one has to make the R code faster and efficient.

2. Introduction to Data Science by Rafael Irizarry- Introducing the concepts and skills for solving data analysis challenges, this book covers the concepts of probability, statistical interference, linear regression and machine learning. Moreover, this book assist in developing skills pertaining to R programming, data wrangling with dplyr, data visualization with ggplot2 and algorithm building with caret amongst others.

3. Cookbook for R by Winston Chang- Being a fantastic resource for getting started about plotting with ggplot and more, this book offers answers to lots of coding questions, which arise while making publication quality graphics with R.

4. Data Visualization: A practical introduction by Kieran Healy- Offering a hands-on introduction about visualization data using R and Wickham’s ggplot, this book assist in building the visualisations for data science piece by piece, from simple scatter plots to more complex graphics.

5. Exploratory Data Analysis with R by Roger D Peng- Based on the courses from John Hopkins Data Science Specialization, this book covers the basics in exploratory analysis, and topics needed for analyzing and visualising high-dimensional or multi-dimensional data like Hierarchial clustering, K-means clustering, and dimensionality reduction techniques-SVD and PCA. View More