Courses Using R

Most courses last 4 weeks and consist of readings, supplemental materials, exercises, and a private discussion forum with fellow students and the instructor. Readings and homework are released on a week-by-week basis, and during each week there are no particular times when you must be online. At the end of each week, you submit your homework (you'll get marks and individual feedback in a few days).

Courses in R fall into the following categories:


I. Using R as a statistical package


II. Learning how to program in R - for those new to programming


III. Building R programming skills - for those familiar with R, or experienced with other programming languages or statistical computing environments


IV. Applying R to specific domains or applications

Thanks to Gregory Piatetsky at KDNuggets for his annual software poll: R is the most popular overall tool among data miners, although Python usage is growing rapidly according to the nearly 3,000 voters.   The particiation by region was: US/Canada (41.5%), Europe (38.4%), Asia (8.2%), Latin America (6.3%), Australia/NZ (3.1%), Africa/MidEast (2.5%). 

Top Analytics Tools and Trends

Here are the top 10 tools by share of usage: 

Top10 Analytics Data Mining Software 2015 

The top 10 tools by share of users were

  1. R, 46.9% share ( 38.5% in 2014)
  2. RapidMiner, 31.5% ( 44.2% in 2014)
  3. SQL, 30.9% ( 25.3% in 2014)
  4. Python, 30.3% ( 19.5% in 2014)
  5. Excel, 22.9% ( 25.8% in 2014)
  6. KNIME, 20.0% ( 15.0% in 2014)
  7. Hadoop, 18.4% ( 12.7% in 2014)
  8. Tableau, 12.4% ( 9.1% in 2014)
  9. SAS, 11.3 (10.9% in 2014)
  10. Spark, 11.3% ( 2.6% in 2014)



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