MOC 20773
Microsoft 20773 - Analyzing Big Data with Microsoft R

Course Description

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.

Who should Attend:

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.

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

Lessons

  • What is Microsoft R server
  • Using Microsoft R client
  • The ScaleR functions
Lab : Exploring Microsoft R Server and Microsoft R Client

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

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

Module 4: Processing Big Data

Explain how to transform and clean big data sets.

Lessons

  • Transforming Big Data
  • Managing datasets
Lab : Processing big data

Module 5: Parallelizing Analysis Operations

Explain how to implement options for splitting analysis jobs into parallel tasks.p>

Lessons

  • Using the RxLocalParallel compute context with rxExec
  • Using the revoPemaR package
Lab : Using rxExec and RevoPemaR to parallelize operations

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

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.

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

Prerequisites

This course requires that you meet the following prerequisites:

  • 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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