Introduction to R and Revolution R for data mining (TRA35)
Monday, January 13, 2014 from 9:00 AM to 5:00 PM (PST)
Mountain View, CA
San Francisco, California
London, United Kingdom
R, the premier language for computational statistics has also evolved into powerful and popular tool for data mining. Much of R's core functionality is focused on exploring and understanding data, model design, inference, and visualization and is directly applicable to any data mining effort. But most importantly, the large number of predictive analytics algorithms that have already been implemented in R and the ease with which new algorithms may be developed make R an essential data mining tool. When enhanced with the big data capabilities of Revolution Enterprise 6.0 R becomes the platform of choice for serious data mining projects.
In this fast paced course, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques and some programming skills can become immediately productive in R. The class uses a combination of lecture and labs to instruct students on how to effectively use R for Data Mining. In addition, students will have homework assignments between the sessions to practice the concepts learned.
- 1 day
- Practicing Data Miners new to R
- R users who want to learn more about the powerful Revolution R Enterprise
- Data mining students with strong programming skills
- Basic understanding of various Data Mining Techniques.
- Programming experience in some language
- Windows Laptop/Desktop with Revolution R Enterprise installed.
- Revolution Analytics Training Center Requirements
- Overview of the R language and data mining resources.
- Using the rattle GUI to get started with Data Mining and R
- Data structures in R
- R functions and basic statistics
- Data Exploration
- Introduction to clustering algorithms
- Introduction to classification algorithms
- Homework Assignments
- Introduction to the Caret package
- Overview of the RevoScaleR package
- Reading data to and from .Xdf files
- The RevoScale R Data Step
- Kmeans with RevoScaleR
- Logistic Regression with RevoScaleR
- Resources for further study
About Joseph Rickert, the Instructor
Joseph is a Product Marketing Manager at Revolution Analytics with a passion for analyzing data. He has worked a number of successful Silicon Valley start-ups including Sytek, Alantec, Parallan Computer and Scotts-Valley Instruments. He taught statistics briefly at SJSU. He blogs at blog.revolutionanalytics.com.
- MSc Statistics, Cal State.
- MA, Humanities, Cal State.
- BA Mathematics, Franklin & Marshall College.
Areas of Expertise:
- Big data analytics and visualization
We have the right to cancel the event for any reason at any time. Revolution Analytics will refund all monies paid for ticket sales in full in the event of a cancellation. We are not responsible for any travel related expenses incurred by attendees for this event. This includes but not limited to transportation, hotel accommodations or any other travel related expenses secured by the attendee, due to a cancellation on our part.
- 30 or more days from the event date: Full refund less 10%
- 16-29 days from the event date: 50% refund
- 15 or less days from the event date: No refund
- all related transaction fees PayPal and Eventbrite are not refundable
- discount offers cannot be combined
- A student ID Number is not a proof of full time university enrollment to get the student’s discount. Proof of enrollment in 9 units or more on a current academic registration document will be required to receive the student's discount.