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Programming Statistical Applications in R

An introductory course that teaches the foundations of scientific and statistical programming using R software.
(55 reviews)
1,666 students
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Platform: Udemy
Video: 11h 10m
Language: English
Next start: On Demand

Table of contents


Programming Statistical Applications in R is an introductory course teaching the basics of programming mathematical and statistical applications using the R language. The course makes extensive use of the Introduction to Scientific Programming and Simulation using R (spuRs) package from the Comprehensive R Archive Network (CRAN). The course is a scientific-programming foundations course and is a useful complement and precursor to the more simulation-application oriented R Programming for Simulation and Monte-Carlo Methods Udemy course. The two courses were originally developed as a two-course sequence (although they do share some exercises in common). Together, both courses provide a powerful set of unique and useful instruction about how to create your own mathematical and statistical functions and applications using R software.
Programming Statistical Applications in R is a “hands-on” course that comprehensively teaches fundamental R programming skills, concepts and techniques useful for developing statistical applications with R software. The course also uses dozens of “real-world” scientific function examples. It is not necessary for a student to be familiar with R, nor is it necessary to be knowledgeable about programming in general, to successfully complete this course. This course is ‘self-contained’ and includes all materials, slides, exercises (and solutions); in fact, everything that is seen in the course video lessons is included in zipped, downloadable materials files. The course is a great instructional resource for anyone interested in refining their skills and knowledge about statistical programming using the R language. It would be useful for practicing quantitative analysis professionals, and for undergraduate and graduate students seeking new job-related skills and/or skills applicable to the analysis of research data.
The course begins with basic instruction about installing and using the R console and the RStudio application and provides necessary instruction for creating and executing R scripts and R functions. Basic R data structures are explained, followed by instruction on data input and output and on basic R programming techniques and control structures. Detailed examples of creating new statistical R functions, and of using existing statistical R functions, are presented. Boostrap and Jackknife resampling methods are explained in detail, as are methods and techniques for estimating inference and for constructing confidence intervals, as well as of performing N-fold cross validation assessments of competing statistical models. Finally, detailed instructions and examples for debugging and for making R programs run more efficiently are demonstrated.

You will learn

✓ Understand how to create and manipulate R data structures used in scientific programming applications.
✓ Understand and use important statistical R programming concepts such as looping and control structures, interactive data input and formatting output, writing functions as programs, writing output to a file and plotting output.
✓ Understand and be able to use the R apply family of functions efficiently.
✓ Know how to debug programs and how to make programs run more efficiently.
✓ Understand and be able to implement various resampling methods effectively, including bootstrapping, jackknifing and N-fold cross validation.


• Students will need to install the popular no-cost R Console and RStudio software (instructions provided).

This course is for

• You do NOT need to be experienced with R, nor do you need to have experience with computer programming to successfully complete this course.
• The course would be useful to anyone interested in learning more about statistical programming using the R language.
• Course is good for undergraduate students seeking to acquire programming skills and knowledge of R software.
• Course is useful for graduate students seeking to acquire and refine their skills relating to data analysis and manipulation.
Associate Professor of Information Systems
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 an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). 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.
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Platform: Udemy
Video: 11h 10m
Language: English
Next start: On Demand

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