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M20773 Analyzing Big Data with Microsoft R

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.


Before attending this course, students must 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
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
  • What is Microsoft R server
  • Using Microsoft R client
  • The ScaleR functions
  • Using R client in VSTR and RStudio
  • Exploring ScaleR functions
  • Connecting to a remote server
Module 2: Exploring Big Data
  • Understanding ScaleR data sources
  • Reading data into an XDF object
  • Summarizing data in an XDF object
  • 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
  • Visualizing In-memory data
  • Visualizing big data
  • Using ggplot to create a faceted plot with overlays
  • Using rxlinePlot and rxHistogram
Module 4: Processing Big Data
  • Transforming Big Data
  • Managing datasets
  • Transforming big data
  • Sorting and merging big data
  • Connecting to a remote server
Module 5: Parallelizing Analysis Operations
  • Using the RxLocalParallel compute context with rxExec
  • Using the revoPemaR package
  • Using rxExec to maximize resource use
  • Creating and using a PEMA class
Module 6: Creating and Evaluating Regression Models
  • Clustering Big Data
  • Generating regression models and making predictions
  • 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
  • Creating partitioning models based on decision trees
  • Test partitioning models by making and comparing predictions
  • Splitting the dataset
  • Building models
  • Running predictions and testing the results
  • Comparing results
Module 8: Processing Big Data in SQL Server and Hadoop
  • Using R in SQL Server
  • Using Hadoop Map/Reduce
  • Using Hadoop Spark
  • 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
Course length
3 days (24 hours)

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