20773 - Analyzing Big Data with Microsoft R

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.

• 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
Explain how to visualize data by using graphs and plots.

• 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
Explain how to transform and clean big data sets.

• 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
Explain how to implement options for splitting analysis jobs into parallel tasks.

• 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
Explain how to build and evaluate regression models generated from big data.

• 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
Explain how to create and score partitioning models generated from big data.

• 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
Explain how to transform and clean big data sets.

• 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.