推荐丨Big R：从数据科学到机器学习和大数据

本文转载自公众号经济政策模拟

发现一本新书，事关R语言，

是在2021年出版，还是推荐给同行

THE BIG R-BOOK：FROM DATA SCIENCE TO LEARNING MACHINES AND BIG DATA

作者是：Philippe J.S. De Brouwer

目录

Foreword About the Author

Acknowledgements Preface

About the Companion Site I Introduction

1 The Big Picture with Kondratiev and Kardashev 

2 The Scientific Method and Data

3 Conventions

II Starting with R and Elements ofStatistics 

4 The Basics of R

5 Lexical Scoping and Environments 

6 The Implementation ofOO 

7 Tidy R with the Tidyverse

8 Elements of Descriptive Statistics 

9 Visualisation Methods

10 Time Series Analysis

11 Further Reading

III Data Import 

12 A Short History ofModern Database Systems 

13 RDBMS

14 SQL 

15 Connecting R to an SQL Database

IV DataWrangling 

16 Anonymous Data

17 DataWrangling in the tidyverse 

18 Dealing with Missing Data 

19 Data Binning

20 Factoring Analysis and Principle Components 

V Modelling

21 Regression Models

22 Classification Models

23 Learning Machines

24 Towards a TidyModelling Cycle with modelr 

25 Model Validation

26 Labs 27 Multi Criteria Decision Analysis (MCDA)

VI Introduction to Companies 

28 Financial Accounting (FA) 

29 Management Accounting 30 Asset Valuation Basics

VII Reporting

31 A Grammar ofGraphics with ggplot2

32 RMarkdown

33 knitr and LATEX 

34 An Automated Development Cycle 

35 Writing and Communication Skills 

36 Interactive Apps

VIII Bigger and Faster R 

37 Parallel Computing 

38 R and Big Data

39 Parallelism for Big Data 

40 The Need for Speed

IX Appendices

A Create your own R package 

B Levels ofMeasurement

C Trademark Notices

D Code Not Shown in the Body ofthe Book 

E Answers to Selected Questions Bibliography Nomenclature

Index

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 Rincludes 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.

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