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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 139
Veröffentlichungsjahr: 2016
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Table of Contents
R Data Science Essentials
R Data Science Essentials
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: January 2016
Production reference:1040116
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B32PB, UK.
ISBN 978-1-78528-654-4
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Credits
Authors
Raja B. Koushik
Sharan Kumar Ravindran
Reviewers
Jeremy Gray
Navin K Manaswi
Commissioning Editor
Dipika Gaonkar
Acquisition Editor
Manish Nainan
Content Development Editor
Mehvash Fatima
Technical Editor
Suwarna Patil
Copy Editor
Tasneem Fatehi
Project Coordinator
Shipra Chawhan
Proofreader
Safis Editing
Indexer
Mariammal Chettiyar
Graphics
Disha Haria
Production Coordinator
Arvindkumar Gupta
Cover Work
Arvindkumar Gupta
About the Authors
Raja B. Koushik is a business intelligence professional with over 7 years of experience and is currently working in one of the leading international IT services companies. His primary interest lies for business intelligence technologies, such as ETL, reporting, and dashboarding, along with analytics based on statistics. He has worked with one of the world's largest companies for both their U.S. as well as UK business in the healthcare and leasing domains. He holds an engineering degree with specialization in information technology from Anna University.
I would like to thank my friends, for I don't know how far I would have come without you guys. I would like to thank Sharan, for giving me this opportunity and also to the Packt team for their constant support. I would like to dedicate this book to Saranya, my wife, for always believing in me and for being so encouraging and supportive of my endeavours; to Shravani, my little bundle of joy, for all the joy and happiness that she has given me; last but not the least, to my parents, Mr Boopalan and Mrs Geetha, without you both I am nothing.
Sharan Kumar Ravindran is a data scientist with over 5 years of experience and is currently working with a leading e-commerce company in India. His primary interest lies in statistics and machine learning, and he has worked with multiple customers across Europe and the U.S. in the e-commerce and IoT domains. He holds an MBA degree with specialization in marketing and business analysis. He conducts workshops, partnering with Anna University, to train their staff, research scholars, and volunteers in analytics. In addition to co-authoring Data Science Essentials with R by Packt Publishing, Sharan has also co-authored Mastering Social Media Mining with R by Packt Publishing. He maintains www.rsharankumar.com, a website with links to his social profiles and data blog.
I would like to thank all my friends, colleagues, and family members, without whom I wouldn't have learned as much as I did. I would also like to thank the readers of my first book, Mastering Social Media Mining, whose feedback helped me a lot. I would like to specially thank my mother, dad, wife, and sister for all the support they provided. I would like to dedicate this book to my grandparents, son, and niece.
About the Reviewers
Jeremy Gray is a data scientist with over 8 years of experience and is based in Toronto.
He completed his PhD in biology at the University of Auckland (the birthplace of R) and worked as a post-doctoral fellow and course instructor at the University of Toronto. His research interests are primarily in using R as an integrated machine learning environment, financial modeling, and consumer analytics, as well as pedagogical methods in scientific computing.
I would like to thank my wonderful fiancé, Mandy Cheema, for her support during the reviewing of this book.
Navin K Manaswi is a data science professional who loves to delve into messy complex data to bring meaningful insights out of it. Although he has been recognized as one of the top 10 data scientists in India, he still loves to learn everyday as a curious child does. Having done both his bachelor's and master's from IIT Kanpur, he has been contributing to the world of data analytics, machine learning, big data technologies, and business intelligence. So far, he has worked at the intersection of technologies and business domains of supply chain management, sales and marketing, finance, and healthcare.
I would like to thank my mother, Smt. Geeta, for invaluable guidance.
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Preface
According to an article in Harvard Business Review, a data scientist's job is the best job of the 21st century. With the massive explosion in the amount of data generated, and with organizations becoming increasingly data-driven, the requirement for data science professionals is ever increasing.
R Data Science Essentials will provide a detailed step-by-step guide to cover various important concepts in data science. It covers concepts such as loading data from different sources, carrying out fundamental data manipulation techniques, extracting the hidden patterns in data through exploratory data analysis, and building complex, predictive, and forecasting models. Finally, you will learn to visualize and communicate the data analysis to an audience. This book is aimed at beginners and intermediate users of R, taking them through the most important techniques in data science that will help them start their data scientist journey.
What this book covers
Chapter 1, Getting Started with R, introduces basic concepts such as loading the data to R from different sources, implementing various preprocessing techniques to handle missing data and outliers, and managing data from different sources by merging and subsetting it. It also covers arithmetic and string operations in R. Overall, this chapter will help you convert the data to a usable format that can be consumed for further data analysis and model building.
Chapter 2, Exploratory Data Analysis, introduces different statistical techniques that assist not only in the better understanding of the data, but also help in developing intuition about the dataset by summarizing and visualizing the important characteristics of the variables in the dataset.
Chapter 3, Pattern Discovery, focuses on techniques to extract patterns from the raw data as well as to derive sequential patterns hidden in the data. This chapter will touch on the evaluation metrics and the tweaking of parameters to adjust the rank of the association rules. This chapter also discusses the business cases where these techniques can be used.
Chapter 4, Segmentation Using Clustering, demonstrates how and when to perform a clustering analysis, how to identify the ideal number of clusters for a dataset, and how the clustering can be implemented using R. It also focuses on hierarchical clustering and how it is different from normal clustering. You will also learn about the visualization of clusters.
Chapter 5, Developing Regression Models, demonstrates why regression models are used and how logistic regression is different from linear regression. It shows you how to implement regression models using R and also explores the various methods used to check the fit accuracy. It touches on the different methodologies that can be used to improve the accuracy of the model.
Chapter 6, Time Series Forecasting, explains forecasting from fundamentals such as converting the normal data frame to a time series data and shows you methods that help uncover the hidden patterns in time series data. It will also teach you the implementation of different algorithms for the forecasting.
Chapter 7, Recommendation Engine, shows you the basic idea behind a recommendation engine and some of the real-life use cases in the first part of the chapter. In the latter part of the chapter, the popular collaborative filtering algorithm based on items as well as users is explained in detail along with its implementation.
Chapter 8, Communicating Data Analysis, covers some of the best ways to communicate the results to the user, such as how to make data visualization better using packages in R such as ggplot and googleViz, and demonstrates stitching together the visualizations by creating an interactive dashboard using R shiny.
What you need for this book
In order to make your learning efficient, you need to have a computer with Windows, Ubuntu, or OS X.
You need to download R to execute the code mentioned in this book. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the code was written using RStudio. RStudio is an integrated development environment (IDE) for R and can be downloaded from http://www.rstudio.com/products/rstudio/.
Who this book is for
If you are an aspiring data scientist or analyst who has a basic understanding of data science and basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.
Conventions
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
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Note
Warnings or important notes appear in a box like this.
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Chapter 1. Getting Started with R
R is one of the most popular programming languages used in computation statistics, data visualization, and data science. With the increasing number of companies becoming data-driven, the user base of R is also increasing fast. R is supported by over two million users worldwide.
In this book, you will learn how to use R to load data from different sources, carry out fundamental data manipulation techniques, extract the hidden patterns in data through exploratory data analysis, and build complex predictive as well as forecasting models. Finally, you will learn to visualize and communicate the data analysis to the audience. This book is aimed at beginners and intermediate users of R, taking them through the most important techniques in data science that will help them start their data scientist journey.
