Big R-Book - From Data Science to Learning Machines and Big Data

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.

Table des matières

Foreword xxv
 
About the Author xxvii
 
Acknowledgements xxix
 
Preface xxxi
 
About the Companion Site xxxv
 
I Introduction 1
 
1 The Big Picture with Kondratiev and Kardashev 3
 
2 The Scientific Method and Data 7
 
3 Conventions 11
 
II Starting with R and Elements of Statistics 19
 
4 The Basics of R 21
 
5 Lexical Scoping and Environments 81
 
6 The Implementation of OO 87
 
7 Tidy R with the Tidyverse 121
 
8 Elements of Descriptive Statistics 139
 
9 Visualisation Methods 159
 
10 Time Series Analysis 197
 
11 Further Reading 211
 
III Data Import 213
 
12 A Short History of Modern Database Systems 215
 
13 RDBMS 219
 
14 SQL 223
 
15 Connecting R to an SQL Database 253
 
IV Data Wrangling 257
 
16 Anonymous Data 261
 
17 Data Wrangling in the tidyverse 265
 
18 Dealing with Missing Data 333
 
19 Data Binning 343
 
20 Factoring Analysis and Principle Components 363
 
V Modelling 373
 
21 Regression Models 375
 
22 Classification Models 387
 
23 Learning Machines 405
 
24 Towards a Tidy Modelling Cycle with modelr 469
 
25 Model Validation 475
 
26 Labs 495
 
27 Multi Criteria Decision Analysis (MCDA) 511
 
VI Introduction to Companies 563
 
28 Financial Accounting (FA) 567
 
29 Management Accounting 583
 
30 Asset Valuation Basics 597
 
VII Reporting 683
 
31 A Grammar of Graphics with ggplot2 687
 
32 R Markdown 699
 
33 knitr and LATEX 703
 
34 An Automated Development Cycle 707
 
35 Writing and Communication Skills 709
 
36 Interactive Apps 713
 
VIII Bigger and Faster R 741
 
37 Parallel Computing 743
 
38 R and Big Data 761
 
39 Parallelism for Big Data 767
 
40 The Need for Speed 793
 
IX Appendices 819
 
A Create your own R package 821
 
B Levels of Measurement 829
 
C Trademark Notices 833
 
D Code Not Shown in the Body of the Book 839
 
E Answers to Selected Questions 845
 
Bibliography 859
 
Nomenclature 869
 
Index 881

A propos de l'auteur

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.

Résumé

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.

Détails du produit

• Auteurs: Philippe J S. De Brouwer, Philippe J S De Brouwer, Philippe J S. De Brouwer, Philippe J. S. De Brouwer, Pjs De Brouwer
• Edition: Wiley, John and Sons Ltd
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 31.03.2020
• EAN: 9781119632726
• ISBN: 978-1-119-63272-6
• Pages: 928
• Catégories: Sciences naturelles, médecine, informatique, technique > Mathématiques > Théorie des probabilités, stochastique, statistiques; Sciences sociales, droit, économie > Economie > Economie privée; Statistik, Datenanalyse, Statistics, Business & management, data analysis, Wirtschaft u. Management, Theorie der Entscheidungsfindung, Decision Sciences, R (Programm), Statistiksoftware / R, Statistical Software / R