Analyzing Big Data with Microsoft R (20773A)

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.

Prerequisites
In addition to their professional experience, students who attend this course should 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.

Objectives
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  
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Course Outline

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

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

Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
Lessons
  • Visualizing In-memory data
  • Visualizing big data

Module 4: Processing Big Data
Explain how to transform and clean big data sets.
Lessons
  • Transforming Big Data
  • Managing datasets

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

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

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

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