Course overview
 Provider
 Udemy
 Course type
 Paid course
 Level
 All Levels
 Duration
 11 hours
 Lessons
 89 lessons
 Certificate
 Available on completion
 Course author
 Geoffrey Hubona, Ph.D.

 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 Nfold cross validation.
Description
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 scientificprogramming foundations course and is a useful complement and precursor to the more simulationapplication oriented R Programming for Simulation and MonteCarlo Methods Udemy course. The two courses were originally developed as a twocourse 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 "handson" 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 "realworld" 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 'selfcontained' 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 jobrelated 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 Nfold cross validation assessments of competing statistical models. Finally, detailed instructions and examples for debugging and for making R programs run more efficiently are demonstrated.
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