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The Big R-Book

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Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for pra... Weiterlesen
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Beschreibung

Introduces professionals and scientists to statistics and machine learning using the programming language R

Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. 

The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.

  • Provides a practical guide for non-experts with a focus on business users
  • Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting
  • Uses a practical tone and integrates multiple topics in a coherent framework
  • Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R
  • Shows readers how to visualize results in static and interactive reports
  • Supplementary materials includes PDF slides based on the book's content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site

The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.



PHILIPPE J.S. DE BROUWER, PHD, is director at HSBC, guest professor at four universities and MBA programs (University of Warsaw, Jagiellonian University, Krakow School of Business and AGH University of Science and Technology) and honorary consul for Belgium in Krakow. As a professor, he builds bridges not only between universities and the industry, but also across disciplines. He teaches mathematicians leadership skills and non-mathematicians coding. As a scientist, he tries to combine research on financial markets, psychology, and investments to the benefit of the investor. As an honorary consul he is passionate about serving the community and helping initiatives grow.

Autorentext

PHILIPPE J.S. DE BROUWER, PHD, is director at HSBC, guest professor at four universities and MBA programs (University of Warsaw, Jagiellonian University, Krakow School of Business and AGH University of Science and Technology) and honorary consul for Belgium in Krakow. As a professor, he builds bridges not only between universities and the industry, but also across disciplines. He teaches mathematicians leadership skills and non-mathematicians coding. As a scientist, he tries to combine research on financial markets, psychology, and investments to the benefit of the investor. As an honorary consul he is passionate about serving the community and helping initiatives grow.

Klappentext

Introduces professionals and scientists to statistics, machine learning, and big data using the programming language R

Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.

The Big R-Book: From Data Science to Learning Machines and Big Data includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling and exploring data. In Part 5 we learn to build models, Part 6 introduces the reader to the reality in companies, Part 7 covers reports and interactive applications and Part 8 introduces the reader to big data and performance computing. The appendices focus on specialist topics such as building your own extention for R, answer questions that appear througout the book, etc.

  • Provides a practical guide for non-experts with a focus on business users
  • Contains a unique combination of topics including an introduction to R, machine learning, multi criteria decision analysis, mathematical models, data wrangling, and reporting
  • Uses a practical tone and integrates multiple topics in a coherent framework
  • Demystifies the hype around machine learning and AI by enabling readers to understand the models and program them in R
  • Shows readers how to visualize results in reports and dynamic websites
  • Supplementary materials include PDF slides based on the book's content on an Wiley Instructor-only Book Companion Site, as well as all the extracted R-code available to everyone on a Wiley Student Book Companion Site

The Big R-Book is an excellent guide for science technology, engineering, or mathematics students and graduates who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models or review them.

Inhalt

Foreword v

About the Author vii

Acknowledgements ix

Preface / Why this book? xi

Contents xv

I Introduction 1

1 The Big Picture with Kondratiev and Kardashev 3

2 The Scientific Method and Data 7

3 Conventions 13

II Starting with R and Elements of Statistics 19

4 The Basics of R 21

4.1 Variables 27

4.2 Data Types 29

4.2.1 Elementary Data Types 29

4.2.2 Vectors 30

4.2.3 Lists 33

4.2.4 Matrices 39

4.2.5 Arrays 42

4.2.6 Factors 44

4.2.7 Data Frames 48

4.3 Operators 56

4.3.1 Arithmetic Operators 56

4.3.2 Relational Operators 57

4.3.3 Logical Operators 57

4.3.4 Assignment Operators 59

4.3.5 Other Operators 60

4.3.6 Loops 62

4.3.7 Functions 66

4.3.8 Packages 70

4.3.9 Strings 73

4.4 Selected Data Interfaces 76

4.4.1 CSV Files 76

4.4.2 Excel Files 80

4.4.3 Databases 80

4.5 Distributions 83

4.5.1 Normal Distribution 83

4.5.2 Binomial Distribution 85

5 Lexical Scoping and environments 91

5.1 Environments in R 92

5.2 Lexical Scoping in R 94

6 The Implementation of OO 99

6.1 Base Types 102

6.2 S3 Objects 104

6.2.1 Creating S3 objects 107

6.2.2 Creating generic methods 109

6.2.3 Method dispatch 110

6.2.4 Group generic functions 111

6.3 S4 Objects 114

6.3.1 Creating S4 Objects 114

6.3.2 Recognising objects, generic functions, and methods 122

6.3.3 Creating S4 Generics 124

6.3.4 Method dispatch 125

6.4 The reference class, refclass, RC or R5 model 127

6.4.1 Creating R5 objects 127

6.5 OO Conclusion 134

7 Tidy R with the Tidyverse 137

7.1 The Philosophy of the Tidyverse 138

7.2 Packages in the tidyverse 141

7.3 Working with the tidyverse 144

7.3.1 tibbles 144

7.3.2 Piping with R 150

7.3.3 Attention points when using the pipe command 151

7.3.3.1 Advanced piping 153

7.3.3.2 Conclusion 155

8 Elements of Descriptive Statistics 157

8.1 Measures of Central Tendency 158

8.1.1 Mean 158

8.1.2 The Median 161

8.1.3 The Mode 162

8.2 Measures of Variation or Spread 164

8.3 Measures of Covariation 166

8.4 Chi Square Tests 169

9 Further Reading 171

III Data Import 173

10 A short history of modern database systems 175

11 RDBMS 179

12 SQL 183

12.1 Designing the database 184

12.2 Building the database 187

12.3 Adding data to the database 196

12.4 Querying the database 200

12.5 Modifying an existing database 206

12.6 Advanced features of SQL 211

13 Connecting R to an SQL database 215

IV Data Wrangling 221

14 Anonymising Data 225

15 DataWrangling in the tidyverse 229

15.1 Tidy data 230

15.2 Importing the data 232

15.2.1 Importing from an SQL RDBMS 232

15.2.2 Importing flat files in the tidyverse 234

15.2.2.1 CSV Files 236

15.2.2.2 Making sense of fixed width files 238

15.3 Tidying up data with tidyr 243

15.3.1 Splitting tables 244

15.3.2 headers to data 249

15.3.3 Spreading one column over many 250

15.3.4 separate 252

15.3.5 Unite 254

Produktinformationen

Titel: The Big R-Book
Untertitel: From Data Science to Learning Machines and Big Data
Autor:
EAN: 9781119632771
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (epub)
Hersteller: Wiley
Genre: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Anzahl Seiten: 928
Veröffentlichung: 16.10.2020
Dateigrösse: 28.5 MB