Geoffrey Hubona, Ph.D. Courses
R Programming for Simulation and Monte Carlo Methods
4.0 (470 Reviews)
Provider: Udemy
Time: 11.7h- 2020
$64.99
VisitData Mining with R: Go from Beginner to Advanced!
4.4 (428 Reviews)
Provider: Udemy
Time: 11.9h- 2020
$74.99
VisitThe Comprehensive Programming in R Course
4.3 (268 Reviews)
Provider: Udemy
Time: 25h- 2020
$10.99
VisitThe Comprehensive Statistics and Data Science with R Course
4.1 (261 Reviews)
Provider: Udemy
Time: 19.7h- 2019
$54.99
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Most Recent Review
Great well rounded course for comprehensive understanding of R. My only issues is that occasionally, the video ended in the middle of the sentence and the exercises where relatively easy compared to some of the material presented in the videos.
- Review by Matej Buršík on The Comprehensive Programming in R Course
Instructor Bio
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is a tenured associate professor of MIS and Data Analytics at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Programming using Python (undergraduate), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate), among other courses. In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.




