Analyzing Big Data with Microsoft R

Microsoft

Microsoft

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As the training provider to many of Australia’s large corporate organizations, Streamtech Knowledge is the one most highly skilled training institution for Microsoft technologies in Australia. Offering a wider variety of Certified Microsoft courses than any other IT training organization, scheduled more often and with more certified and highly experienced MCT trainers on our staff, Streamtech Knowledge likes to make the business of acquiring skills as easy, flexible and convenient as possible.

Code

20773

Revision

A

Duration

3 Days

Audience

Data Professionals

Level

Expert

Price

$2,500.00

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

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.

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
Labs
  • Using R client in VSTR and RStudio
  • Exploring ScaleR functions
  • Connecting to a remote server

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

3 Visualizing Big Data

Explain 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

4 Processing Big Data

Explain 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

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
Labs
  • Using rxExec to maximize resource use
  • Creating and using a PEMA class

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
Labs
  • Creating a cluster
  • Creating a regression model
  • Generate data for making predictions
  • Use the models to make predictions and compare the results

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
Labs
  • Splitting the dataset
  • Building models
  • Running predictions and testing the results
  • Comparing results

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
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