Implementing Monte Carlo Method in R

Some problems are difficult or impossible to solve with standard mathematical or statistical approaches - these can often be solved by using the Monte Carlo method. This course covers multiple Monte Carlo applications for ready use.
Course info
Level
Advanced
Updated
Apr 28, 2020
Duration
1h 42m
Table of contents
Description
Course info
Level
Advanced
Updated
Apr 28, 2020
Duration
1h 42m
Description

Repeated sampling using the Monte Carlo method can be a much more efficient approach in solving difficult problems vs. standard mathematical or statistical practices. In this course, Implementing Monte Carlo Method in R, you’ll gain the ability to build your own Monte Carlo simulations using a variety of approaches and know which solution is most effective. First, you’ll explore the basics behind Monte Carlo and the fundamental functions in R. Next, you’ll discover some simple methods, followed by simulations on stock and commodities data for estimating return probabilities. Finally, you’ll learn how to use Monte Carlo methods on A/B tests. When you’re finished with this course, you’ll have the skills and knowledge of Monte Carlo methods needed to implement these methods yourself.

About the author
About the author

Chase is currently Lead Data Scientist at Tesorio and formerly was an Assistant Professor of Finance and Economics at the University of South Carolina Upstate.

More from the author
Building Successful Virtual Teams
Beginner
1h 48m
Jun 11, 2020
Designing Data Pipelines with TensorFlow 2.0
Intermediate
1h 53m
Mar 12, 2020
More courses by Chase DeHan
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
everyone. My name is Chase de Han and welcome to my course Monte Carlo method In order, I'm currently the manager data science at Sisario and Hold a PhD in economics from the University of Utah. In this course, we're going to cover multiple implementations of the Monte Carlo method. In our, the Montecarlo method is an incredibly valuable approach to estimating uncertainty and numerical outputs that might be difficult or impossible using other methods. Some of the major topics that we will cover include fundamentals of Monte Carlo and the essential are functions. The basic Monte Carlo approaches with rolling dice or estimating pie. Then we're going to get in some fun topics with estimating asset prices and value at risk in financial applications for stock and commodity prices. And it will close it out by using Monte Carlo on a B testing for cases where you might not have enough data to perform with traditional statistical methods by the end of this course will be able to implement Monte Carlo methods in a variety of approaches. Before beginning this course, you should be quite familiar with the our language and have the ability to use and write functions I hope you join me on this journey to learn Monte Carlo on implementing Monte Carlo method and our course at Pluralsight.