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Microsoft Course 20773: Analyzing Big Data with Microsoft R

Course Length: 3 days

Class Schedule
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  • Hands-on instruction by a certified instructor
  • Includes all course materials
  • On-site Testing
  • Lunch & Snacks provided each day
  • 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.

    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

    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.

    Course Outline

    Module 1: Microsoft R Server and R Client

    Explain how Microsoft R Server and Microsoft R Client work.

    • What is Microsoft R server
    • Using Microsoft R client
    • The ScaleR functions

    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.

    • Understanding ScaleR data sources
    • Reading data into an XDF object
    • Summarizing data in an XDF object

    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.

    Visualizing In-memory data Visualizing big data

    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.

    • Transforming Big Data
    • Managing datasets

    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.

    • Using the RxLocalParallel compute context with rxExec
    • Using the revoPemaR package

    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

    • Clustering Big Data
    • Generating regression models and making predictions

    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.

    • Creating partitioning models based on decision trees.
    • Test partitioning models by making and comparing predictions

    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.

    • Using R in SQL Server
    • Using Hadoop Map/Reduce
    • Using Hadoop Spark

    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.

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