Introduction to R for Data Mining (Taught by Revolution Analytics – 31,000+ Views)
Revolution Analytics is often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people they have identified three factors that greatly facilitate learning R.
For a quick start:
- Find a way of orienting yourself in the open source R world
- Have a definite application area in mind
- Set an initial goal of doing something useful and then build on it
Introduction to R Programming Part 1 (Taught by Stanford 12,000+ views)
Instructor: David Ruau, PhD – http://www.stanford.edu/people/druau
By the end of parts I and II, participants will be able to:
- Interact with R using commands passed through the console
- Import and export data in various formats and transform those data in R
- Make statistical graphics plots (and more)
- ·Write small scripts and functions using the R language.
***Part 1 starts at 1 min and 50 seconds with about 28 minutes of tech support to get the program installed. Formal lecture begins at 30 minutes and 30 seconds.
For a complete description of the classes & Installing “R” and other packages prior to the class: please see instructions for “Introduction to R programming I & II course” [pdf] http://elane/laneconnex/public/media/…
More on R:
High Performance Predictive Analytics in R and Hadoop
Presented August 27, 2013
Hadoop is rapidly being adopted as a major platform for storing and managing massive amounts of data, and for computing descriptive and query types of analytics on that data. However, it has a reputation for not being a suitable environment for high performance complex iterative algorithms such as logistic regression, generalized linear models, and decision trees.
At Revolution Analytics, we think that reputation is unjustified, and in this webinar, you will learn the approach we have taken to porting our suite of High Performance Analytics algorithms to run natively and efficiently in Hadoop.