R Programming for Data Science

Roger Peng

ISBN 10: 1365056821 / ISBN 13: 9781365056826

Published by Lulu.Com, 2016

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. Seller Inventory # 9781365056826

Bibliographic Details

Title: R Programming for Data Science

Publisher: Lulu.Com

Publication Date: 2016

Binding: Taschenbuch

Condition: Neu

Edition: 5th or later Edition

About this title

Synopsis:

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.