IL - MOC 20773 - Analyzing Big Data with Microsoft R
Instructor-Led Training
Intermediate
3 days
Onsite or Virtual
Course 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.
Objectives
  • 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
Pre-Requisites
  • 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.

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
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 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
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 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
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 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
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 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
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 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
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 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
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 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
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.

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