MOC On-Demand: 20773-Analyzing Big Data with Microsoft R - On-Demand Course

Section 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.

Topics :
• 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

Section 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.

Topics :
• 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

Section 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.

Topics :
• Visualizing In-memory data
• Visualizing big data

Lab :
• Visualizing data
• Using ggplot to create a faceted plot with overlays
• Using rxlinePlot and rxHistogram

Section 4: Processing Big Data
Explain how to transform and clean big data sets.

Topics :
• Transforming Big Data
• Managing datasets

Lab :
• Processing big data
• Transforming big data
• Sorting and merging big data
• Connecting to a remote server

Section 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.

Topics :
• 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

Section 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data

Topics :
• 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

Section 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.

Topics :
• 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

Section 8: Processing Big Data in SQL Server and Hadoop
Explain how to transform and clean big data sets.

Topics :
• 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