Big Data Analytics with Microsoft R Training

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 analyse 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.

At course completion

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

• Explain how Microsoft R Server and Microsoft R Client work.
• Lessons
  • What is Microsoft R server?
  • Using Microsoft R client
  • The Scale R functions
• Lab: Exploring Microsoft R Server and Microsoft R Client
  • Using R client in VSTR and RStudio
  • Exploring Scale R functions
  • Connecting to a remote server

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 Scale R 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

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 plot to create a faceted plot with overlays
  • Using rxlinePlot and rxHistogram

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

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

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

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