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Analyzing Big Data with Microsoft R - 20773 Course Outline

 (3 days)

Overview
The main purpose of the course is to give students the ability to use Microsoft R Server to create and run an analysis on a large dataset, and show how to utilize it in Big Data environments, such as a Hadoop or Spark cluster, or a SQL Server database.

Audience profile
The primary audience for this course is people who wish to analyze large datasets within a big data environment.
The secondary audience are developers who need to integrate R analyses into their solutions.

Special Note to New Hampshire Residents
This course has not yet been approved by the State's Department of Education. Please contact us to get an update as to when the class should be available in New Hampshire.

Prerequisites
In addition to their professional experience, students who attend this course should have:
Programming experience using R, and familiarity with common R packages
Knowledge of common statistical methods and data analysis best practices.
Basic knowledge of the Microsoft Windows operating system and its core functionality.

Working knowledge of relational databases.

At course completion
After completing this course, students will be able to:
Explain how Microsoft R Server and Microsoft R Client work
Use R Client with R Server to explore big data held in different data stores
Visualize data by using graphs and plots
Transform and clean big data sets
Implement options for splitting analysis jobs into parallel tasks
Build and evaluate regression models generated from big data
Create, score, and deploy partitioning models generated from big data
Use R in the SQL Server and Hadoop environments

Course Outline

Module 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.
Lessons
What is Microsoft R server
Using Microsoft R client
The ScaleR functions
Lab : Exploring Microsoft R Server and Microsoft R Client
Using R client in VSTR and RStudio
Exploring ScaleR functions
Connecting to a remote server

Module 2: Exploring Big Data
At the end of this module the student will be able to use R Client with R Server to explore big data held in different data stores.
Lessons
Understanding ScaleR data sources
Reading data into an XDF object
Summarizing data in an XDF object
Lab : Exploring Big Data
Reading a local CSV file into an XDF file
Transforming data on input
Reading data from SQL Server into an XDF file
Generating summaries over the XDF data

Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
Lessons
Visualizing In-memory data
Visualizing big data
Lab : Visualizing data
Using ggplot to create a faceted plot with overlays
Using rxlinePlot and rxHistogram

Module 4: Processing Big Data
Explain how to transform and clean big data sets.
Lessons
Transforming Big Data
Managing datasets
Lab : Processing big data
Transforming big data
Sorting and merging big data
Connecting to a remote server

Module 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.
Lessons
Using the RxLocalParallel compute context with rxExec
Using the revoPemaR package
Lab : Using rxExec and RevoPemaR to parallelize operations
Using rxExec to maximize resource use
Creating and using a PEMA class

Module 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data
Lessons
Clustering Big Data
Generating regression models and making predictions
Lab : Creating a linear regression model
Creating a cluster
Creating a regression model
Generate data for making predictions
Use the models to make predictions and compare the results

Module 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.
Lessons
Creating partitioning models based on decision trees.
Test partitioning models by making and comparing predictions
Lab : Creating and evaluating partitioning models
Splitting the dataset
Building models
Running predictions and testing the results
Comparing results

Module 8: Processing Big Data in SQL Server and Hadoop
Explain how to transform and clean big data sets.
Lessons
Using R in SQL Server
Using Hadoop Map/Reduce
Using Hadoop Spark
Lab : Processing big data in SQL Server and Hadoop
Creating a model and predicting outcomes in SQL Server
Performing an analysis and plotting the results using Hadoop Map/Reduce
Integrating a sparklyr script into a ScaleR workflow
View outline in Word

A20773

Attend hands-on, instructor-led Analyzing Big Data with Microsoft R - 20773 training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

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Fee:  $1595

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