Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

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.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


The basic concept of system dynamics is to predict time based on given scenarios/assumptions.

We looked at simple applications for population demographics using microsimulation modeling. Microsimulation modeling became popular since managers and politicians wanted to have predictions of the future. While this was done in the past with aggregated information, with agent-based microsimulation models we do it on an individual level. A criticism of this is that statistical uncertainty is not taken into account and that selected scenarios are unlikely to be true in the future since political changes or unobservable events may happen that cannot be considered beforehand.

Dynamic systems are popular in business and finance but they also play a central role in ecological research. We looked at the Lotka-Volterra model in one example that the author of this book regularly observes in a place in the Upper Austrian mountains. But most importantly, and ironically speaking, we have shown the relationship...