Coupon & course info
Course Name: R Programming for Simulation and Monte Carlo Methods
Subtitle: Learn to program statistical applications and Monte Carlo simulations with numerous “real-life” cases and R software.
Instructor: Taught by Geoffrey Hubona, Professor of Information Systems
Subcategory: Data & Analytics
Provided by: Udemy
Price: $49 (before discount)
Free coupon code: See above (no charge for coupon)
Review info & popularity
As of March 9, 2016…
Students: 741 students enrolled
Ratings: 3 reviews
Rank: ranked #592 in Udemy Business Courses
Brief course description
R Programming for Simulation and Monte Carlo Methods focuses on using R software to program probabilistic simulations, often called Monte Carlo Simulations. Typical simplified “real-world” examples include simulating the probabilities of a baseball player having a ‘streak’ of twenty sequential season games with ‘hits-at-bat’ or estimating the likely total number of taxicabs in a strange city when one observes a certain sequence of numbered cabs pass a particular street corner over a 60 minute period. In addition to detailing half a dozen (sometimes amusing) ‘real-world’ extended example applications, the course also explains in detail how to use existing R functions, and how to write your own R functions, to perform simulated inference estimates, including likelihoods and confidence intervals, and other cases of stochastic simulation. Techniques to use R to generate different characteristics of various families of random variables are explained in detail. The course teaches skills to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo Integration, and variance reduction techniques. The course partially utilizes the Comprehensive R Archive Network (CRAN) spuRs package to demonstrate how to structure and write programs to accomplish mathematical and probabilistic simulations using R statistical software.
(Read more about this course on the official course page.)
Geoffrey Hubona bio
Dr. Geoffrey Hubona has been a full-time faculty member at 3 major state universities in the Eastern United States for 20 years, teaching dozens of different statistics, business information systems, and computer science courses to undergraduate, master’s and PhD students. He earned a PhD in Information Systems and Computer Science (1993) from the University of South Florida in Tampa, FL (1993); an MA in Economics (1990) from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He held full-time faculty positions at the University of Maryland Baltimore County (1993-1996), Virginia Commonwealth University (1996-2001), and Georgia State University (2001-2010). He is the founder of the Georgia R School (2010), a non-profit online educational institution that teaches research methods and quantitative analysis techniques such as linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is recognized as an expert of the analytical, open-source R software suite and of various path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.
(Learn more about this instructor on the official course page.)
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Final details for this Udemy course
Skill level: All Levels
Lectures: 107 lessons
Duration: 11.5 Hours of video
What you get: Use R software to program probabilistic simulations, often called Monte Carlo simulations.
Target audience: You do NOT need to be experienced with R software and you do NOT need to be an experienced programmer.
Requirements: Students will need to install the popular no-cost R Console and RStudio software (instructions provided).
Access: Lifetime access
Peace of mind: 30 day money back guarantee
Availability: available online, as well as on iOS and Android
Download options: check course to see if you can download lessons