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Analyzing Big Data with Microsoft R

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  • Analyzing Big Data with Microsoft R
    3 days  (Instructor Led Online)  |  Analytics and Data Management Courses

    Course Details

    With the Analyzing Big Data with Microsoft R course you will learn how 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. In this course, we will show you how to use MRS to run an analysis on a large dataset and provide some examples of how to deploy it on a Spark cluster or a SQL Server database. Upon completion, you will know how to use R for big-data problems.

     

    This training already retired on 30th of June 2019.

    See other Microsoft courses

    Objectives

    After completing this course, you 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

    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

     

    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

     

    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

    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

     

    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 analysis and plotting the results using Hadoop Map/Reduce
    • Integrating a sparklyr script into a ScaleR workflow

    Prerequisites

    Before taking this course you 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.