MOC 20773 - Analyzing Big Data with Microsoft R

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Training for your Team

3
Days
  • Private Class for your Team
  • Online or On-location
  • Customizable
  • Expert Instructors
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.

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.

Goals
  1. Learn to explain how Microsoft R Server and Microsoft R Client work.
  2. Learn to use R Client with R Server to explore big data held in different data stores.
  3. Learn to visualize data by using graphs and plots.
  4. Learn to transform and clean big data sets.
  5. Learn to implement options for splitting analysis jobs into parallel tasks .
  6. Learn to build and evaluate regression models generated from big data .
  7. Learn to create, score, and deploy partitioning models generated from big data.
  8. Learn to use R in the SQL Server and Hadoop environments.
Outline
  1. Microsoft R Server and R Client
    1. What is Microsoft R server
    2. Using Microsoft R client
    3. The ScaleR functions
    4. Lab: Exploring Microsoft R Server and Microsoft R Client
      1. Using R client in VSTR and RStudio
      2. Exploring ScaleR functions
      3. Connecting to a remote server
  2. Exploring Big Data
    1. Understanding ScaleR data sources
    2. Reading data into an XDF object
    3. Summarizing data in an XDF object
    4. Lab: Exploring Big Data
      1. Reading a local CSV file into an XDF file
      2. Transforming data on input
      3. Reading data from SQL Server into an XDF file
      4. Generating summaries over the XDF data
  3. Visualizing Big Data
    1. Visualizing In-memory data
    2. Visualizing big data
    3. Lab: Visualizing data
      1. Using ggplot to create a faceted plot with overlays
      2. Using rxlinePlot and rxHistogram
  4. Processing Big Data
    1. Transforming Big Data
    2. Managing datasets
    3. Lab: Processing big data
      1. Transforming big data
      2. Sorting and merging big data
      3. Connecting to a remote server
  5. Parallelizing Analysis Operations
    1. Using the RxLocalParallel compute context with rxExec
    2. Using the revoPemaR package
    3. Lab: Using rxExec and RevoPemaR to parallelize operations
      1. Using rxExec to maximize resource use
      2. Creating and using a PEMA class
  6. Creating and Evaluating Regression Models
    1. Clustering Big Data
    2. Generating regression models and making predictions
    3. Lab: Creating a linear regression model
      1. Creating a cluster
      2. Creating a regression model
      3. Generate data for making predictions
      4. Use the models to make predictions and compare the results
  7. Creating and Evaluating Partitioning Models
    1. Creating partitioning models based on decision trees.
    2. Test partitioning models by making and comparing predictions
    3. Lab: Creating and evaluating partitioning models
      1. Splitting the dataset
      2. Building models
      3. Running predictions and testing the results
      4. Comparing results
  8. Processing Big Data in SQL Server and Hadoop
    1. Using R in SQL Server
    2. Using Hadoop Map/Reduce
    3. Using Hadoop Spark
    4. Lab: Processing big data in SQL Server and Hadoop
      1. Creating a model and predicting outcomes in SQL Server
      2. Performing an analysis and plotting the results using Hadoop Map/Reduce
      3. Integrating a sparklyr script into a ScaleR workflow
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this SQL Server class:

  • 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.
Preparing for Class
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