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R Training

Course Date and Location
Big Data Analytics with Revolution R Enterprise

Portfolio: Big Data | Standard Course Duration: 8 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

Learn how to take advantage of the capabilities of Revolution R Enterprise 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 Revolution R Enterprise functions for big data analyses. In addition, you will learn how to visualize the results through the use of graphical packages.

Audience

  • R users who have mastered the basics of R
  • Data Analysts, Data scientists, Data Miners, Statisticians, R
  • Programmers

Prerequisites

  • Foundational knowledge in R language including hands-on experience
  • Understanding of multivariate modeling methods such as linear and logistic regression
Not there yet? Try our courses in the Introductory portfolio
 
  • Revolution Analytics Mountain View, CA
    Classroom course
  • Revolution Analytics Mountain View, CA
    Classroom course
  • Bangalore, India
    Classroom course
  • Las Vegas, NV
    Classroom course
  • London, UK
    Classroom course
  • Mountain View, CA
    Classroom course
  • New Delhi, India
    Classroom course
Data Visualization in R

Portfolio: Data Mining | Standard course duration: 8 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This course introduces you to the wide variety of packages for example, base packages, lattice package, ggplot2 (grammar of graphics) package and iPlots, googleVis and Ggobi to create compelling graphs, interactive graphics and visualizations.

Audience

  • Data Miners,
  • Data Scientists
  • Statisticians
  • BI developers

Prerequisites

  • Introductory course in R or equivalent experience

Not there yet? Try our courses in the Introductory portfolio.

  • Mountain View, CA
    Classroom course
Fundamentals in R language

Portfolio: Introductory | Standard course duration: 8 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This course will get you started in programming using R language. You will learn how to manipulate data and plot various graphs.

This is a hands-on course filled with real data and examples, case studies, and in-class mini projects.

Audience

  • New users to R language

Prerequisites

  • Foundational knowledge in programming and statistics

 

 

  • Dallas, TX
    Classroom course
  • Mountain View, CA
    Classroom course
  • Las Vegas, NV
    Classroom course
Implementing Web Services with DeployR
Portfolio: Programming | Standard course duration: 8 hours | Delivery formats available for this course: Classroom or Virtual
 
Course Overview

Revolution Analytics DeployR is designed for R users and IT professionals looking to deploy R applications on a server for access by client applications through a web services API. This class uses a combination of lecture and exercises to install, configure, and manage DeployR server instance. In addition, you will learn considerations for writing web services-based R applications, how to install and manage them within the DeployR server environment, and lastly how to access them through client-based API’s.

Audience

  • R users who have mastered the basics of R.
  • R programmers

Prerequisites

  • Familiarity with at least one web services protocol stack, one client based programming language for accessing web services and intermediate knowledge on R.

 

Not there yet? Try our courses in the Introductory portfolio.

  • Mountain View, CA
    Classroom course
Introduction to R

Portfolio: Introductory | Standard course duration: 16 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This course will get you started in programming using R language. You will learn how to manipulate data, plot various graphs, perform statistical modeling and understand key efficiency concepts to optimize R programs. This is a hands-on course filled with real data and examples, case studies, and in-class mini projects.

Audience

  • Data Analysts
  • Modelers,
  • Statisticians who are new to R

Prerequisites

  • Foundational knowledge in programming and statistics
  • February 05 - 06 , 2014
    Austin, TX
    Classroom course
  • February 17 - 18 , 2014
    Mountain View, CA
    Classroom course
  • Bangalore, India
    Classroom course
  • Chicago, IL
    Classroom course
  • Mountain View, CA
    Classroom course
  • San Diego, CA
    Classroom course
  • New Delhi, India
    Classroom course
Introduction to R and Revolution R for Data Mining

Portfolio: Introductory | Standard course duration: 16 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

R, the premier language for computational statistics has evolved into powerful and popular tool for data mining. R is focused on exploring and understanding data, model design, inference, and visualization and is directly applicable to any data mining effort. The number of predictive analytics algorithms already been implemented in R and the ease with which new algorithms can be developed make R an essential data mining tool. In this course, you will first learn basic commands in R for data management, data exploration. You will also practice using graphical user interface (Rattle) for basic data mining operations and caret package for more sophisticated requirements. Finally, you will use Revolution R Enterprise for data mining with big data.

Audience

  • Practicing Data Miners new to R
  • Data mining students with strong programming skills

Prerequisites

  • Basic Understanding of various Data Mining Techniques
  • Programming experience in some language
  • Windows Laptop/Desktop with Revolution R Enterprise installed
  • Mountain View, CA
    Classroom course
Introduction to R for SAS users

Portfolio: Introductory | Standard course duration: 16 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This course will get you started in programming using R language. You will learn how to manipulate data replacing the SAS data step, plot various graphs, replace common SAS exploratory and manipulation procedures, perform statistical modeling and understand key efficiency concepts to optimize R programs. This is a hands-on course filled with real data and examples, case studies, and in-class mini projects.

Audience

  • SAS users converting to R Data Analysts, Modelers, Statisticians who are new to R

Prerequisites

Attendees should know:

  • How to program in SAS, SPSS or Stata, and
  • Be familiar with basic statistical methods including linear regression and basic analysis of variance.
  • March 11 - 14 , 2014
    Virtual course
  • London, UK
    Classroom course
Introduction to R for SAS, SPSS and Stata Users

Portfolio: Programming | Standard course duration: 16 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This workshop introduces R in a way that takes advantage of what you already know. For many topics we will begin with add-on commands that work similarly to your current software. Then we will cover R’s built-in commands that provide simpler but more flexible output. We will also discuss aspects of R that are likely to trip you up. For example, many R functions let you specify which data set to use in a way that looks identical to SAS, but which differs in a way that is likely to lead you to perplexing error messages.

We will devote most of our time to working through examples that you may run simultaneously on your computer. However, handouts will include each step and its output if you prefer instead just relax and take notes. Most examples come from the books R for SAS and SPSS Users, R for Stata Users, and http://r4stats.com. That makes it easy to review what we did later with full explanations, or to learn more about a particular subject by extending an example, which you have already learned. After each 4-hour session you will receive a set of practice exercises for you to do on your own time, as well as solutions to the problems. The instructor will be available after the workshop via email to address these problems or any other topic in the workshop.

Audience

  • Data Analysts
  • Data scientists
  • Statisticians and Programmers who are already familiar with another analytic platform

Prerequisites

Attendees should:

  • Know how to program in SAS, SPSS, or Stata, and
  • Be familiar with basic statistical methods including linear regression and basic analysis of variance
  • January 13 - 15 , 2014
    Virtual course
  • April 21 - 23 , 2014
    Virtual course
Managing Data with R

Portfolio: Introductory | Standard course duration: 4 hours | Delivery format available for this course: Virtual

Course Overview

Before you can analyze data, it must be in the right form. Getting it into that form is often where we spend most of our time. This workshop shows how to perform the most commonly used data management tasks in R. We will cover how to use R’s popular add-on packages and compare them to R’s older built-in functions. Most of our time will be spent working through examples that you may run simultaneously on your computer. However, the handouts include each step and its output, so feel free to just relax and take notes.

Most examples come from the extensive data management examples in the books, R for SAS and SPSS Users, R for Stata Users, and the web site, http://r4stats.com. That makes it easy to review what we did later with full explanations, or to learn more about a particular subject by extending an example, which you have already learned. At the end of the workshop, you will receive a set of practice exercises for you to do on your own time, as well as solutions to the problems. The instructor is available via email to address these problems or any other topics in his workshops or books.

Audience

  • Data Analysts,
  • Modelers,
  • Statisticians

Prerequisites

  • Attendees should know basic R programming, including how to read data files and call functions
  • Virtual course
  • Virtual course
Parallel Computing with Revolution R Enterprise

Portfolio: Big Data | Standard course duration: 8 hours | Delivery format available for this course: Classroom or Virtual

Course Overview

This course provides you an overview of techniques for parallel computing with R on computer clusters, multi-core systems or in grid computing. The combination of lecture and exercises in this class helps you to effectively use and script parallel programming packages in R.

You will learn how use open source tools in R and Revolution R Enterprise to manage and run parallelization processes.

Audience

  • Data Analysts
  • Data scientists
  • Data Miners
  • Statisticians
  • R-Programmers

Prerequisites

  • Familiarity with the basics of the Revolution R Enterprise
  • Basic understanding of parallel programming
  • Bring a multi-core laptop or have access to a server cluster
  • Mountain View, CA
    Classroom course
Revolution R Enterprise For Data Mining

Portfolio: Data Mining | Standard course duration: 8 hours | Delivery formats available for this course: Classroom or Virtual

Course Overview

This course focuses on data mining as the application area and shows how anyone with just a basic knowledge of elementary data mining techniques and some programming skills can become immediately productive in Revolution R Enterprise.

In this course, you will first learn about basic commands, R resources and data mining operations using Rattle GUI. After an overview of Revolution R Enterprise, you will perform basic statistics and modeling operations and practice algorithms for data mining.

Audience

  • Practicing Data Miners new to R
  • Data mining students with strong programming skills

Prerequisite

  • Foundational knowledge in R language including hands-on experience
  • Understanding of multivariate modeling methods such as linear and logistic regression
  • Dallas, TX
    Classroom course
  • Mountain View, CA
    Classroom course
Using R with Hadoop

Portfolio: Big Data | Standard course duration: 8 hours | Delivery format available for this course: Classroom or Virtual

Course Overview

This course provides the experienced R programmer with an introduction to the use of R in the Hadoop environment through the RHadoop packages. This includes an overview and practical examples of use of the rhdfs, rhbase, and rmr packages for accessing the Hadoop’s Distributed File System (HDFS), interact with the HBase NoSQL database,  and writing Map Reduce jobs from R respectively. The class uses a combination of lecture and labs.

Audience

  • Data Analysts
  • Data scientists using Hadoop environment

Prerequisites

  • Functional knowledge in R language including hands-on experience
  • Basic understanding of Hadoop
  • December 09 - 13 , 2013
    Virtual course
  • Dallas, TX
    Classroom course
  • Mountain View, CA
    Classroom course