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.

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Spis treści

Simulation for Data Science with R. Effective Data-driven Decision Making eBook -- spis treści

• 1. Introduction
• 2. R and high-performance computing
• 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 of complex data
• 11. System dynamics