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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...
Read more
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...
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/tpu1ok/implementing
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Teaches fundamental functions in R
Develops skills and knowledge for implementing these methods
Apply Monte Carlo to practical business problems, such as stock and commodities data for estimating return probabilities
Suitable for learners with basic knowledge of statistics
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Career center
Learners who complete Implementing Monte Carlo Method in R will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts, also known as "quants", create and test mathematical models to predict investment risk. These models are based on a foundation of statistical methods, such as the Monte Carlo method that is taught in this course. Because of the heavy usage of mathematics and statistics to successfully complete the responsibilities of a Quant, this course will help to lay a useful foundation.
Financial Analyst
Financial Analyst
Financial Analysts make investment recommendations and analyze risk. They build mathematical and statistical models, like the ones you will learn in this course, to aid in this process. This course will provide you with some of the necessary skills needed to become a Financial Analyst.
Actuary
Actuary
Actuaries analyze and manage financial risks. They use mathematical and statistical methods, like the Monte Carlo method, to do so. This course will help future actuaries, or current actuaries, to develop the advanced skills required to succeed in this field.
Risk Manager
Risk Manager
Risk Managers monitor and assess risks in all areas of an organization. They use mathematical and statistical models to develop strategies to mitigate these risks. The Monte Carlo method is one of the statistical methods that Risk Managers use. This course will help current and future Risk Managers to develop the advanced skills they need.
Data Scientist
Data Scientist
Data Scientists solve business problems through the use of data analysis and machine learning. They use mathematical and statistical methods, such as the Monte Carlo method, to build machine learning models that perform predictive analytics. This course will help future Data Scientists, as well as current Data Scientists, to develop critical skills in the field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical methods to develop models and draw conclusions from data. The Monte Carlo method is one of the statistical methods that statisticians use, and this course will help future and current statisticians to develop important skills in this area.
Business Analyst
Business Analyst
Business Analysts analyze business data and systems to develop solutions to business problems. They use mathematical and statistical methods to help businesses achieve their goals, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand customer behavior and trends. They use statistical methods to develop and test hypotheses, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Economist
Economist
Economists study economic data to understand economic trends. They use mathematical and statistical methods to develop models and draw conclusions from data, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software. They use mathematical and statistical methods to develop and test software, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to solve business problems. They use these methods to develop and test models and optimize processes, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use mathematical and statistical methods to develop and test models, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. They use mathematical and statistical methods to develop and test models, including the Monte Carlo method. This course may be useful to you if you'd like to enter this field.
For more career information including salaries, visit:
OpenCourser.com/course/tpu1ok/implementing
Reading list
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Provides a detailed overview of Monte Carlo methods, specifically in the context of financial engineering. The book focuses on a variety of topics relevant to this field, including option pricing, risk management, and stochastic processes.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in applied statistics. The authors cover a wide range of topics, including Bayesian inference, missing data imputation, and sensitivity analysis.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in computational finance. The author covers a wide range of topics, including option pricing, risk management, and portfolio optimization.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on statistical applications. The authors present a wide range of topics, including Markov chain Monte Carlo, importance sampling, and Bayesian inference. The book is suitable for both beginners and experienced researchers.
Provides a detailed overview of Monte Carlo methods, specifically in the context of climate modeling. The book focuses on a variety of topics relevant to this field, including climate sensitivity, extreme events, and climate projections.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in polymer science. The authors cover a wide range of topics, including polymer simulations, phase transitions, and transport properties.
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This paper is one of the most influential papers in the history of computational physics. The authors introduce the Monte Carlo method to the field of quantum mechanics, providing a new tool for solving complex quantum systems.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in statistical physics. The authors cover a wide range of topics, including phase transitions, critical phenomena, and transport properties.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in simulation and optimization. The author covers a wide range of topics, including random number generation, variance reduction techniques, and Markov chain Monte Carlo.
E. Handschin provides a gentle introduction to Monte Carlo methods.
Reference guide for Markov chain Monte Carlo methods, with computer programs and examples.
A comprehensive textbook on Bayesian data analysis that uses Markov chain Monte Carlo methods.
Comprehensive reference guide on Monte Carlo simulation and its applications in finance.
A detailed account of numerical methods in finance, including Monte Carlo methods.
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Comprehensive textbook on Monte Carlo methods.
Textbook on Monte Carlo methods and simulation.
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Covers quadrature methods such as the Monte Carlo method and quasi-Monte Carlo methods.
Comprehensive reference guide on quasi-Monte Carlo methods with a focus on applications in computational finance and numerical integration.
Provides a comprehensive overview of Monte Carlo methods, with a particular focus on applications in systems biology. The author covers a wide range of topics, including stochastic modeling, parameter estimation, and network inference.
Provides a comprehensive overview of Monte Carlo simulation, including its history, theory, and applications. It valuable resource for anyone who wants to learn about or use Monte Carlo simulation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tpu1ok/implementing
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Time
1 hours
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Pluralsight
This course belongs to a
collection
called
Building Statistical and Mathematical Models with R. It's comprised of nine courses:
- Understanding Statistical Models and Mathematical Models
- Solving Problems with Numerical Methods
- Applying the Mathematical MASS Model with R
- Applying Differential Equations and Inverse Models with R
- Performing Dimension Analysis with R
- Applying Linear Algebra with R
- Building Statistical Summaries with R
- Implementing Bootstrap Methods in R
- Implementing Monte Carlo Method in R (viewing)
Good to know
Teaches fundamental functions in R
Develops skills and knowledge for implementing these methods
Apply Monte Carlo to practical business problems, such as stock and commodities data for estimating return probabilities
Suitable for learners with basic knowledge of statistics
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