Simulation for Data Science with R: Effective Data-driven Decision Making

Matthias Templ

€41.99

4.2 (5 Ratings)

Paperback Jun 2016 398 pages 1st Edition

Matthias Templ

€41.99

4.2 (5 Ratings)

Paperback Jun 2016 398 pages 1st Edition

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Key benefits

Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies
A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulation

Description

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.

Who is this book for?

This book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.

What you will learn

The book aims to explore advanced R features to simulate data to extract insights from your data.
Get to know the advanced features of R including high-performance computing and advanced data manipulation
See random number simulation used to simulate distributions, data sets, and populations
Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations
Applications to design statistical solutions with R for solving scientific and real world problems
Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.

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Frequently bought together

Learning Probabilistic Graphical Models in R

€29.99

€45.99

€41.99

Total € 117.97

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Roland R. Wieser Jul 21, 2016

For many years I teach students in Statistics (with R).This book is great for self-study. It is unique from content and has a high educational value. I was ever searching for a book that teaches Comutational Statistics (with R) so practical and elementary.You can feel while reading that the author is an experienced educator and scientist. I can fully recommend this book. It should be in the library of any statistician.

Amazon Verified review

Duncan W. Robinson Sep 06, 2016

Simulation for Data Science with R successfully fills an area of data science that has not traditionally had much coverage other than snippets here in there in broader texts. It shows the reader how to use resampling methods, perform hypothesis testing via bootstrap, demonstrate probability theory using simulation & use Monte Carlo methods for optimization. The theory is fairly concisely explained, the code is plentiful & the potential uses are vast. I highly recommend this book!

Barbara Szabo Sep 21, 2016

The book has great strategy, because it introduces various topics by humorous examples, which are easy to be understood and than goes to details of the scientific background of computational statistics and simulations.The reader can1) learn more about the advanced features of R (all examples listed in the book, can be downloaded as code files),2) get advices on choosing the right technique applied for simulation, resampling or hypothesis testing,3) easily understand computer-intense methods and tools applied in statistical simulation via memorable examples,4) gain experience when working on real-world dataIf you are still not convinced from the advantages of this book, let me give you more details about it.It covers the main issues you can face with, when you need a computational and methodological framework for statistical simulation. The topics include simulating distributions and datasets, Monte Carlo methods for inference statistics, microsimulation and dynamical systems, furthermore it present solutions using computer-intense approaches.The author gave unique, excellent examples to increase our interest in scientific methods supported by R codes. The system dynamics for instance explained via the love/hate story of Prince Harry and Chelsy Davy, the optimization problem is introduced via the case of an Australian guy who trying to climb to the highest Austrian mountain and so on.Overall this is an excellent book written in a unique style for readers who look for data-driven solutions in simulation with R.

Amazon Customer Oct 06, 2016

In my studies of mathematics I have always looked for a book describing the complex field of data simulation understandably and with this book I have finally found what I was hoping for: simulatiion methods are described very neat and as a lot of R code is provided I can easily realise described methods in my job as statistician.

Steve Miller Jan 12, 2019

not a good book. returned.

Simulation for Data Science with R: Effective Data-driven Decision Making

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