Simulation For Data Science With R [Paperback]

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 written by the Amazon best-selling author of Learning Statistics (The easier Way) with R

Who This Book Is 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.

In Detail

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.