R Course Finder
Find an R course quickly, using the filters on the right.
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Note: Prices shown for all paid courses on DataCamp platform are monthly subscriptions, which give full access to all courses on this platform.
Showing 1–10 of 25 results
Applied Predictive Analytics$589.00
In this online course, “Applied Predictive Analytics,” you will apply data mining techniques in a real world case study
After taking this online course, “Bayesian Statistics in R” you will be able to install and run rjags, a program for Bayesian analysis within R.
Big Data: Measuring and Predicting Human BehaviourFree!
Join us to explore how the vast amounts of data generated today can help us understand and even predict how humans behave.
Biostatistics in R with Clinical Trial Applications$589.00
This online course, “Biostatistics in R: Clinical Trial Applications” covers the implementation in R of statistical procedures important for the clinical trial statistician.
Case studies in business analytics with ACCENTURE$70.00
We want to ignite your reflection process. Hence, you will best make use of the Accenture cases by watching first the MOOC and then investigate by yourself on the different concepts, industries, or challenges that are introduced during the videos.
Case Studies in Data Mining with R$60.00
Learn to use the “Data Mining with R” (DMwR) package and R software to build and evaluate predictive data mining models.
Case Studies in Functional GenomicsFree!
Explore data analysis of several experimental protocols, using open source software, including R and Bioconductor.
Data Mining in R$589.00
In this online course, “Data Mining in R,” you will learn how to partition data and use a holdout sample, how to measure the performance of predictive models, and what to do about the problem of overfitting
Data Science with R: Data Analysis and Visualization$2,190.00
This course is a 35-hour program designed to provide a comprehensive introduction to R.
Data Science with R: Machine Learning$2,990.00
This 35-hour course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques in R