Call us at 01780 483931
Hands On Technology Transfer, Inc.

Analyzing Big Data with Microsoft R

Attend via Microsoft Official Courses On-Demand (MOC On-Demand)

Travel Packages Discount Programs
Top

Analyzing Big Data with Microsoft R (20773A)

Duration: 3 Days

MOC On-Demand Price: £650 + VAT

Delivery Option: Attend via MOC On-Demand

Students Will Learn

  • Explaining how Microsoft R Server and Microsoft R Client work
  • Using R Client with R Server to explore big data held in different data stores
  • Visualizing data by using graphs and plots
  • Transforming and clean big data sets
  • Implementing options for splitting analysis jobs into parallel tasks
  • Building and evaluating regression models generated from big data
  • Creating, scoring, and deploying partitioning models generated from big data
  • Using R in the SQL Server and Hadoop environments

Course Description

This is a Microsoft Official Course (MOC) and includes Microsoft courseware and hands-on labs. This course gives 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.

Course Prerequisites

Before attending this course, students must 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

About MOC On-Demand

Microsoft Official Courses On-Demand (MOC On-Demand) uses a combination of streaming video, text, lab exercises and assessment checks throughout the course. MOC On-Demand courses are available for 90 days and recommend the following system requirements:

  • Browser: Current version of Internet Explorer, Microsoft Edge, Google Chrome or Firefox
  • Internet: Broadband Internet connection of over 4Mbps
  • Screen Resolution: 1280 x 1024 or higher

Course Overview

Module 1: Microsoft R Server and R Client

Explains how Microsoft R Server and Microsoft R Client work.

Lessons

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

Explains how to visualize data by using graphs and plots.

Lessons

  • Visualizing In-memory data
  • Visualizing big data
Labs
  • 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

Explains how to transform and clean big data sets.

Lessons

  • Transforming Big Data
  • Managing datasets
Labs
  • 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

Explains how to implement options for splitting analysis jobs into parallel tasks.

Lessons

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

Explains how to build and evaluate regression models generated from big data.

Lessons

  • Clustering Big Data
  • Generating regression models and making predictions
Labs
  • 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

Explains 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
Labs
  • 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

Explains how to transform and clean big data sets.

Lessons

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

Course Schedule

Choose a city below to view a schedule. If you have any questions, please call us at 01780 483931 between 9:00 am - 5:00 pm EST.