Analyzing Big Data with Microsoft R M20773

Duration: 
3 days
Codes: 
M20773,R

Overview

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

Delegates will leave with a recognised course certificate

Course Outline

Module 1: Microsoft R Server and R Client

  • Explain how Microsoft R Server and Microsoft R Client work.

Lessons Module 1

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

Lab Module 1 : 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 module1, 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 Module 2

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

Lab Module 2 : 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 module 2, 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 Module 3

  • Visualizing In-memory data
  • Visualizing big data

Lab Module 3 : Visualizing data

  • Using ggplot to create a faceted plot with overlays
  • Using rxlinePlot and rxHistogram
  • After completing module 3, 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 Module 4

  • Transforming Big Data
  • Managing datasets

Lab Module 4 : Processing big data

  • Transforming big data
  • Sorting and merging big data
  • After completing module 4, 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 Module 5

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

Lab Module 5 : Using rxExec and RevoPemaR to parallelize operations

  • Using rxExec to maximize resource use
  • Creating and using a PEMA class
  • After completing module 5, 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 Module 6

  • Clustering Big Data
  • Generating regression models and making predictions

Lab Module 6 : 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 module 6, 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 Module 7

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

Lab Module 7 : Creating and evaluating partitioning models

  • Splitting the dataset
  • Building models
  • Running predictions and testing the results
  • Comparing results
  • After completing module 7, 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

Lessons Module 8

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

Lab Module 8 : Processin

Use ggplot2 to visualize in-memory data Use rxLinePlot and rxHistogram to visualize big data

Transform big data using rxDataStep Perform sort and merge operations over big data sets

Use the rxLocalParallel compute context with rxExec Use the RevoPemaR package to write customized scalable and distributable analytics.

Cluster big data to reduce the size of a dataset. Create linear and logit regression models and use them to make predictions.

Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms. Test partitioning models by making and comparing predictions.

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

Upcoming Dates

  • GREEN This class is Guaranteed To Run.
  • SPVC - Self-Paced Virtual Class.
  • Click a Date to Enroll.
Course Location Days Cost Date
Bristol Bristol3 999 £999 2019-10-02
Devon Exeter3 999 £999 2019-10-02
Midlands Birmingham3 999 £999 2019-10-02
Edinburgh Edinburgh3 999 £999 2019-10-02
Bristol Bristol3 999 £999 2019-12-04
Devon Exeter3 999 £999 2019-12-04
Midlands Birmingham3 999 £999 2019-12-04
Edinburgh Edinburgh3 999 £999 2019-12-04