Analyzing Big Data with Microsoft R (20773A)
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.
Audience Profile
The primary audience for this course is people who wish to analyze large datasets within a big data environment.
The secondary audience are developers who need to integrate R analyses into their solutions.
Prerequisites
In addition to their professional experience, students who attend this course should have:
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Audience Profile
The primary audience for this course is people who wish to analyze large datasets within a big data environment.
The secondary audience are developers who need to integrate R analyses into their solutions.
Prerequisites
In addition to their professional experience, students who attend this course 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.
Working knowledge of relational databases.
Objectives
After completing this course, students 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
Course Outline
Module 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.
Lessons
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
Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
Lessons
Module 4: Processing Big Data
Explain how to transform and clean big data sets.
Lessons
Module 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.
Lessons
Module 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data
Lessons
Module 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.
Lessons
Module 8: Processing Big Data in SQL Server and Hadoop
Explain how to transform and clean big data sets.
Lessons
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
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
Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.
Lessons
- Visualizing In-memory data
- Visualizing big data
Module 4: Processing Big Data
Explain how to transform and clean big data sets.
Lessons
- Transforming Big Data
- Managing datasets
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
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
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
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