20773MOD: Analyzing Big Data with Microsoft R Server

$795

What's included:

  • 90 Day Access
  • Available 24x7
  • HD Quality Streaming Videos
  • On-Demand Labs Hands-On Labs
  • Knowledge Checks

Plus:

  • Official Microsoft Courseware

About this On-Demand Course
This Microsoft On-Demand Course is an integrated on-line training experience that includes video, labs, exercises, text and knowledge checks. Students experience all of this through an on-demand course player.

What's included?

  • Access to the official Microsoft Video On-Demand Course for 90 days from the point of first access, allowing you to start and stop when you need to.
  • Lab access for 90 days from start of access.
  • Digital edition of the Microsoft Official Curriculum (dMOC) manual for reference throughout your course. All dMOC come with fresh editions so your courseware will always be up to date.
  • The content is based on the same official courseware we use in our instructor-led training, and videos feature engaging experts hand-selected by Microsoft. Unlike other on-demand offerings that offer simulated labs, MOC On-Demand gives you a live, real-time environment for hands-on training.

Plus:

  • Digital edition of the Microsoft Official Curriculum (MOC) manual throughout your course. All MOC come with fresh editions so your courseware will always be up to date.

20773MOD: Analyzing Big Data with Microsoft R Server

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:

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.

What You Will Learn:

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 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.