H.L. Gururaj; Kumar V. Ravi; Francesco Flammini; Hong Lin; B. Goutham; Kumar B.R. Sunil; C. Sivapragash Institution of Engineering&Technology (2022) Kovakantinen kirja
Vilas A. Tonapi; Harvinder Singh Talwar; Ashok Kumar Are; B. Venkatesh Bhat; Ch. Ravinder Reddy; Timothy J. Dalton Springer Verlag, Singapore (2021) Kovakantinen kirja
Learn the essence of data science and visualization using R in no time at all
About This Book
* Become a pro at making stunning visualizations and dashboards quickly and without hassle * For better decision making in business, apply the R programming language with the help of useful statistical techniques. * From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns
Who This Book Is For
If you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.
What You Will Learn
* Perform data preprocessing and basic operations on data * Implement visual and non-visual implementation data exploration techniques * Mine patterns from data using affinity and sequential analysis * Use different clustering algorithms and visualize them * Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm * Extract patterns through visualization and build a forecasting algorithm * Build a recommendation engine using different collaborative filtering algorithms * Make a stunning visualization and dashboard using ggplot and R shiny
In Detail
With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Style and approach
This easy-to-follow guide contains hands-on examples of the concepts of data science using R.
