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

The Big R-Book: From Data Science to Learning Machines and Big Data
  • eBook:
    The Big R-Book: From Data Science to Learning Machines and Big Data
  • Author:
    Philippe J. S. De Brouwer
  • Edition:
    1 edition
  • Categories:
  • Data:
    October 27, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    928 pages
  • Format:

Book Description
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.


I - Introduction
1. The Big Picture with Kondratiev and Kardashev
2. The ScientificMethod and Data
3. Conventions

II - Starting with R and Elements of Statistics
4. The Basics of R 21
5. Lexical Scoping and Environments
6. The Implementation of OO
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
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. ClassificationModels
23. Learning Machines
24. Towards a TidyModelling Cycle withmodelr
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 of Graphics 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

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