The Big R-Book: From Data Science to Learning Machines and Big Data
- 13h 56m
- Philippe J. S. De Brouwer
- John Wiley & Sons (US)
- 2021
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.
In this Book
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The Big Picture with Kondratiev and Kardashev
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The Scientific Method and Data
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Conventions
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The Basics of R
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Lexical Scoping and Environments
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The Implementation of OO
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Tidy R with the Tidyverse
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Elements of Descriptive Statistics
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Visualisation Methods
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Time Series Analysis
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Further Reading
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A Short History of Modern Database Systems
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RDBMS
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SQL
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Connecting R to an SQL Database
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Anonymous Data
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Data Wrangling in the tidyverse
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Dealing with Missing Data
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Data Binning
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Factoring Analysis and Principle Components
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Regression Models
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Classification Models
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Learning Machines
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Towards a Tidy Modelling Cycle with modelr
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Model Validation
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Labs
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Multi Criteria Decision Analysis (MCDA)
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Financial Accounting (FA)
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Management Accounting
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Asset Valuation Basics
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A Grammar of Graphics with ggplot2
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R Markdown
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knitr and LATEX
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An Automated Development Cycle
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Writing and Communication Skills
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Interactive Apps
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Parallel Computing
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R and Big Data
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Parallelism for Big Data
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The Need for Speed
