Course Details
This training already retired on 30th of June 2019.
Objectives
After completing this course, you 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
Outline
Module 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
Lab: Exploring Microsoft R Server and Microsoft R Client
- Using R client in VSTR and RStudio
- Exploring ScaleR functions
- Connecting to a remote server
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:
- 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
Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
Lessons:
- Visualizing In-memory data
- Visualizing big data
Lab: Visualizing data
- Using ggplot to create a faceted plot with overlays
- Using rxlinePlot and rxHistogram
Module 4: Processing Big Data
Explain how to transform and clean big data sets.
Lessons:
- Transforming Big Data
- Managing datasets
Lab: Processing big data
- Transforming big data
- Sorting and merging big data
- Connecting to a remote server
Module 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
Lab: Using rxExec and RevoPemaR to parallelize operations
- Using rxExec to maximize resource use
- Creating and using a PEMA class
Module 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
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
Module 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
Lab: Creating and evaluating partitioning models
- Splitting the dataset
- Building models
- Running predictions and testing the results
- Comparing results
Module 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
Lab: Processing big data in SQL Server and Hadoop
- Creating a model and predicting outcomes in SQL Server
- Performing analysis and plotting the results using Hadoop Map/Reduce
- Integrating a sparklyr script into a ScaleR workflow
Prerequisites
Before taking this course you 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.