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Analyzing Big Data with Microsoft R

MOC-20773

In: Data Platform

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.

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.

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.

Prezzo di listino

960,00 EUR + IVA

Esami di certificazione

70773

Durata

  • 21 ore
  • 3 giorni

Prerequisiti

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.

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