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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Chapter 2. R and High-Performance Computing

The software environment R (R Development Core Team, 2015) is nowadays the most commonly used software in the statistical world, and this software is heavily used in this book. The methods described in any of the following chapters are practically applied, and the application of the methods is shown using the statistical environment R. For a book on simulation and data science in R, and to efficiently apply methods, a longer R introduction is needed, especially on features that support efficient calculations.

In this chapter, you will be given a very brief introduction to the functionality of R. This introduction does not replace a general introduction to R but instead shows some useful points, such as introducing modern visualization tools and efficient data manipulation packages. These topics — among others from this chapter — are important for understanding the examples and the R code in the book.

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Simulation for Data Science with R
Published in: Jun 2016
Publisher: Packt
ISBN-13: 9781785881169
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