Analyzing Big Data with Microsoft R 20773

Duration: 
3 days
Codes: 
R,8489
Versions: 
20773

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

  • 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.

Skills Gained

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.

It is recommended that delegates review this self-pace content to gain an introduction to the R language

https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-5

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 : 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 module 8, 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.

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
Onsite
Onsite3 1500 £1500 2018-12-11
Hamburg
Hamburg3 1500 £1500 2018-12-19
Bavaria
Garching3 1500 £1500 2019-01-07
Wallisellen
Wallisellen3 1500 £1500 2019-01-07
London
London3 1656 £1656 2019-01-09
London
London3 1656 £1656 2019-01-09
Wallisellen
Wallisellen3 1500 £1500 2019-01-16
London
London3 1500 £1500 2019-03-18
London
London3 1500 £1500 2019-03-18
Wallisellen
Wallisellen3 1500 £1500 2019-04-24