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Big Data Analysis with Revolution R Enterprise

Intermediate

Updated 10/2023

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8 hours14 videos49 Exercises3,650 XP25,261Statement of Accomplishment

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Course Description

Revolution R Enterprise allows R users to process, visualize, and model terabyte-class data sets at a fraction of the time of legacy products without requiring expensive or specialized hardware. Introductory course for accomplished R users to experience the functionality of Revolution R Enterprise.

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Prerequisites

There are no prerequisites for this course

1

Introduction

Big Data Analytics Overview

Challenges with Big Data

Demonstration of Revolution R Enterprise for Big Data Analytics

File Format

Importing Data with rxImport Function

Functions for Summarizing Data

Creating New Variables using rxDataStep

Transforming Variables Using rxDataStep

Correlations

Linear Regression

How Parallel Exeternal Memory Algorithms in RevoScaleR work

How the Algorithms in RevoScaleR work

2

Data Exploration

Revolution Big Data - Data Exploration Session 1

RevoScaleR options

Import and Explore Dow Jones Data

rxGetInfo Output

Extracting meta data about a variable using rxGetVarInfo()

Summarizing variables with rxSummary()

Revolution Big Data - Data Exploration Session 2

Exploring a Distribution with rxHistogram()

Plotting bivariate relationships with rxLinePlot()

Revolution Big Data - Data Exploration Session 3

Summarzing Variables with rxCrossTabs()

Summarzing Variables with rxCube()

3

Data Manipulation

Revolution Big Data - Data Manipulation Session 1

Concatenating Multiple Datasets to a Single .xdf file

Converting From internal data.frame to external data.frame

Using rxDataStep() to Transform Data

Revolution Big Data - Data Manipulation Session 2

More Complex Transforms Using transformFuncs

Bad transformFunc functions

4

Data Analysis

Data Preparation

Preparing Data For Analysis: Import

Preparing Data For Analysis: Exploration

Linear Model

Construct a linear model

Generating Predictions and Residuals

Compute Standard Errors

Logistic Regression and the GLM

Logistic Regression

Individual Mortgage Information

Higher Default Probability

Individual Higher Default Probability

Valid Arguments for rxGLM

Computing k Means with rxKmeans()

Decision Trees

Create some Decision Trees

Big Data Analysis with Revolution R Enterprise

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