Название: Essentials of R for Data Analytics Автор: Saroj Dahiya Ratnoo, Himmat Singh Ratnoo Издательство: Wiley Год: 2021 Страниц: 620 Язык: английский Формат: epub (true) Размер: 13.1 MB
With widespread and exponential growth of data, people with Data Science background are in great demand. Data analytics, a subdomain of Data Science, is meant to turn data into insight and actionable knowledge. Data analytics mainly deals with exploring, visualizing, transforming and modelling data for making predictions. Learning R is an essential step towards becoming a data analyst.
R is a widely used open-source tool that competes with the other commercially available statistical software. Be it simple data exploration or application of sophisticated statistical and data analytic techniques, the versatility of R is making it an essential tool for working with data. Data Science and data analytics are emerging as essential and much sought after skills in the world today. Data Science includes anything and everything that we can do with data. The focus of data analytics is on answering questions by exploring and transforming data. Building predictive models is another important aspect of data analytics. To be able to realise the full potential of R, it is important to have some level of expertise in R programming so that exasperation does not set in. With no or little knowledge of R programming, any straight away jumping to data analytics may lead to a loss of time and energy in finding and resolving the errors.
This book is for undergraduate and postgraduate students, and for research scholars who wish to learn the essential elements of R programming and its tools for data analytics. Chapter 1 gives an overview of R programming. Chapters 2 to 6 give a detailed introduction to data objects like vectors, factors, matrices, lists and data frames, and the possible ways to manipulate these data objects. Chapter 7 describes how we can get data in and out of R. Chapter 8 is on selection and looping constructs to control the flow of execution. User-defined functions are the building blocks of any program. Writing user-defined functions is taken up in Chapter 9. A picture is worth thousand words for communicating the findings from data; hence Chapters 10 to 12 delve into making of diverse sorts of beautiful graphs and plots. Chapter 13 is on answering interesting questions through data transformations. Predictive modelling is at the heart of data analytics; therefore the book culminates with predictive modelling in Chapters 14 and 15.
The book is written in a user-friendly and learn-by-doing style. While going from the beginning to the end of the book, we can learn by executing the code and getting its output. The output thus obtained can be matched and verified with the output that has been provided right after the code segments.
CHAPTER 1 Getting Started with R CHAPTER 2 Getting Help in R CHAPTER 3 Vectors and Factors in R CHAPTER 4 Matrices in R CHAPTER 5 Lists and Data Frames in R CHAPTER 6 Strings and Dates in R CHAPTER 7 Input Output in R CHAPTER 8 Conditional Statements and Loops in R CHAPTER 9 Writing Functions in R CHAPTER 10 An Introduction to Graphics in R CHAPTER 11 Making Graphs and Charts in R CHAPTER 12 Graphics using ggplot2 CHAPTER 13 Data Transformations in R CHAPTER 14 Predictive Analytics: Classification in R CHAPTER 15 Predictive Analytics: Regression in R Appendix Additional Resources Index
Big Data and Visual Analytics Автор: Sang C. Suh, Thomas Anthony Название: Big Data and Visual Analytics Издательство: Springer Год: 2018 ISBN: 9783319639154 Язык: English Формат:...
Data Analytics and Linux Operating System Название: Data Analytics and Linux Operating System Автор: Isaac D. Cody Издательство: CreateSpace Independent Publishing Platform Год: 2016 Страниц:...
Навигация
Вход на сайт
Реклама
Essentials of R for Data Analytics 
