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Simulation for Data Science with R, by Matthias Templ
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Harness actionable insights from your data with computational statistics and simulations using R
About This Book- 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
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.
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.
Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these methods, and provides computational tools to help you solve common problems in statistical simulation and computer-intense methods.
- Sales Rank: #590361 in Books
- Published on: 2016-06-30
- Released on: 2016-06-30
- Original language: English
- Dimensions: 9.25" h x .90" w x 7.50" l, 1.50 pounds
- Binding: Paperback
- 398 pages
About the Author
Matthias Templ
Matthias Templ is associated professor at the Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology (Austria). He is additionally employed as a scientist at the methods unit at Statistics Austria, and together with two colleagues, he owns the company called data-analysis OG. His main research interests are in the areas of imputation, statistical disclosure control, visualization, compositional data analysis, computational statistics, robustness teaching in statistics, and multivariate methods. In the last few years, Matthias has published more than 45 papers in well-known indexed scientific journals. He is the author and maintainer of several R packages for official statistics, such as the R package sdcMicro for statistical disclosure control, the VIM package for visualization and imputation of missing values, the simPop package for synthetic population simulation, and the robCompositions package for robust analysis of compositional data. In addition, he is the editor of the Austrian Journal of Statistics that is free of charge and open-access. The probability is high to find him at the top of a mountain in his leisure time.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Memorable, practical, suitable for R users
By Barbara Szabo
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 can
1) 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 data
If 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.
0 of 0 people found the following review helpful.
successfully fills an area of data science that has not traditionally had much coverage
By DWR
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!
0 of 0 people found the following review helpful.
Great!
By Amazon Customer
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.
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