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Aron Mesterbasic
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.
Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:
Your expert instructor will get you ready for the following exam and certification, which are included in your course package and covered by the Certification guarantee.
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.
Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.
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 an analysis and plotting the results using Hadoop Map/Reduce
- Integrating a sparklyr script into a ScaleR workflow
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