Analyzing Big Data with Microsoft R (180 Day)

Course Details
Code: ODX20773
Tuition (USD): $1,070.00 $802.50 • Self Paced (0 hours)

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.
You willhave access to all MOC On-Demand content including lab steps, videos,interactions, knowledge checks and assessments for the entire one hundred and eighty(180) days of the course. However, the labs in this course require a MicrosoftAzure subscription. As part of the first lab in the course, an Azure Pass willbe provisioned so you can configure a Microsoft Azure subscription if youchoose not to use an existing subscription. The Azure Pass subscription willlast for up to ninety (90) days upon redemption. So you decide when to redeemthe pass within the one hundred and eighty (180) day period of your MOCOn-Demand access. 
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.

Skills Gained

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 

Who Can Benefit

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.

Prerequisites

Inaddition to their professional experience, students who attend this course shouldhave:

  • Programmingexperience using R, and familiarity with common R packages
  • Knowledgeof common statistical methods and data analysis best practices.
  • Basicknowledge of the Microsoft Windows operating system and its core functionality.

Workingknowledge of relational databases.

Course Details

Outline

Module 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
Aftercompleting this module, students will be able to:
  • Explainthe purpose of R server.
  • Connectto R server from R client
  • Explainthe 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
Aftercompleting this module, students will be able to:
  • Explain ScaleR data sources
  • Describe how to import XDF data
  • Describehow 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
Aftercompleting this module, students will be able to:
  • Use ggplot2 to visualize in-memory data
  • UserxLinePlot 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
Aftercompleting 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
Aftercompleting this module, students will be able to:
  • Use the rxLocalParallel compute context with rxExec
  • Usethe 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 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
Aftercompleting this module, students will be able to:
  • Cluster big data to reduce the size of a dataset.
  • Createlinear 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
Aftercompleting this module, students will be able to:
  • Createpartitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
  • Testpartitioning 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
Aftercompleting 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 toanalyze big data.
Course Details
Code: ODX20773
Tuition (USD): $1,070.00 $802.50 • Self Paced (0 hours)