CONTENUTI:

Module 1: Microsoft R Server and R Client

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

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

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

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

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

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

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

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.