Source: analyticsinsight.net

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.

• 6. Text Mining with R: A Tidy approach by Julia Silge and David Robinson- Being a great introductory book to learn about mining text data with R, this book helps in practicing the principles in text datasets. Moreover, using R and tideverse as examples to explore literature, news, social media data, this book is a must go for learning about text and data analysis, specifically for those who are interested in analysing the social media data.

• 7. An Introduction to Statistical and Data Sciences via R by Chester Ismay and Albert Y.Kim- Covering the basics of statistics for data science using R, this book helps in learning about exploring data, basics of statistics for data science and creating data stories using R.

• 8. Introduction to Empirical Bayes: Examples from Baseball Statistics by David Robinson- Introducing the empirical Bayesian approach for estimating credible intervals, A/B testing and mixture models with R code examples, this book illustrates statistical method for estimating click-through rates on ads, and success of experiments amongst others. This book is a must go if one wants to learn about data science and statistics for data science.

• 9. Data Analysis for the Life Sciences with R by Rafael A Irizarry and Michael I Love – Primarily focusing on high throughput data from genomics, the book helps the reader to solve problems with R code and assist in gaining better intuition behind the math theory. The methods described in this book are best suited for modern data science in any domain.

• 10. Modern Data Science for Modern Biology by Susan Holmes- With only 13 chapters, this book is a comprehensive guide for beginners to learn about R code, theory, and great visualization with ggplot 2. This book also covers various aspects of statistics for data science including, Mixture models, clustering, testing, dimensionality reduction techniques like PCA and SVD.