Course 20773A: Analyzing Big Data with Microsoft R

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Anytime accessYesNo
Anywhere access to recorded instructorYesNo
Microsoft official training contentYesYes
In-depth trainingYesYes
Hands-on labs Yes Yes
SATV redemption Yes Yes
Ask instructor questions in personNoYes
Attend live class in personNoYes
Attend live class remotelyNoYes
Time commitment Self-paced
(3 month access)
3 days
About this course
Audience(s):Data Professional
Technology:SQL Server
Level:Advanced
This Revision:A
Length:3 days
Language(s):English

First published:

26 May 2017
Overview
About this course
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.
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 details
Course OutlineModule 1: Microsoft R Server and R ClientExplain 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

After completing this module, students will be able to:

  • Explain the purpose of R server.
  • Connect to R server from R client
  • Explain the purpose of the ScaleR functions.
Module 2: Exploring Big DataAt 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

After completing this module, students will be able to:

  • Explain ScaleR data sources
  • Describe how to import XDF data
  • Describe how to summarize data held in XCF format
Module 3: Visualizing Big DataExplain 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

After completing this module, students will be able to:

  • Use ggplot2 to visualize in-memory data
  • Use rxLinePlot and rxHistogram to visualize big data
Module 4: Processing Big DataExplain 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

After completing this module, students will be able to:

  • Transform big data using rxDataStep
  • Perform sort and merge operations over big data sets
Module 5: Parallelizing Analysis OperationsExplain 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

After completing this module, students will be able to:

  • Use the rxLocalParallel compute context with rxExec
  • Use the RevoPemaR package to write customized scalable and distributable analytics.
Module 6: Creating and Evaluating Regression ModelsExplain how to build and evaluate regression models generated from big dataLessons
  • 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

After completing this module, students will be able to:

  • Cluster big data to reduce the size of a dataset.
  • Create linear and logit regression models and use them to make predictions.
Module 7: Creating and Evaluating Partitioning ModelsExplain 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

After completing this module, students will be able to:

  • Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
  • Test partitioning models by making and comparing predictions.
Module 8: Processing Big Data in SQL Server and HadoopExplain 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

After completing this module, students will be able to:

  • Use R in the SQL Server and Hadoop environments.
  • Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyze big data.
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.


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