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Bigdata Analytics with Revolution R (for R Users) (TRA36)
Tuesday, September 10, 2013 at 8:00 AM - Thursday, September 12, 2013 at 12:00 PM (PDT)
San Francisco, California
London, United Kingdom
This is a two half day course, held
Tue. 9/10 & Thu. 9/12 from 8:00-12:00 Eastern time
Revo ScaleR, the core of Revolution R Enterprise 6.0 is an R package designed, built and optimized to solve one of R’s biggest soft spots – dealing with big data. Dealing with big data involves addressing several different issues, such as interfacing with diverse data sources, storing and manipulating big data efficiently and implementing statistical algorithms that can handle large data.
This course is designed for R users who have mastered the basics of R and are interested in learning how to take advantage of the capabilities of ScaleR for high performance analytics on datasets that exceed the normal physical memory limits of R. The class uses a combination of lecture and labs to instruct students on how to effectively use and script ScaleR functions for big data analyses. In addition, students will learn how to visualize the results of ScaleR analyses through use of graphics packages.
Users with some knowledge of R and multivariate modeling who would like to apply such techniques to very large datasets using ScaleR.
Familiarity with the basics of the R language and some prior hands-on experience
- Understanding of multivariate modeling methods such as linear and logistic regression.
- Windows Laptop/Desktop with Revolution R Enterprise installed.
Introduction: A taste of the power of Big Data Analytics with Revolution Scale R.
- Discussion on the challenges in big data analytics
- Demonstration of importing and exploring big data
- Simple statistical techniques with big data
- Story Telling with visualizations of big data
- Getting help with Revolution Scale R
Data Munging Lab
- Importing different types of data such as delimited text, fixed format
- Dealing with other data sources – SAS/SPSS files, data frames, ODBC
- Transforming and sub setting big data
- Managing Meta Data and Recoding Variables
- Exporting big data
Data Exploration Lab
- Summarizing big data
- Visualizing big data
- Estimating a model (Linear, Logistic, GLM, k-Means)
- Calculating residuals, plot a histogram of residuals
- Predicting on a new dataset
- Repeating with ‘on the fly’ transformations
- Advanced Data Manipulations
- Working Locally or on a Cluster
- Integrating Revo ScaleR into other R packages
- Building your own models using Revo ScaleR
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.