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.
Learning 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
Pre-Requisites
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.
It is recommended that delegates review this self-pace content to gain an introduction to the R language
https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-5
Course Content
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
Related Courses
- Amazon Web Services – Big Data
- M10990 Analyzing Data with SQL Server Reporting Services
- Building Big data Analytics Solutions in the Hadoop Ecosystem
- Data Science with R Workshop
- M20776 Engineering Data with Microsoft Cloud Services
- Fast Track to Machine Learning
- Java Development on the Ethereum Blockchain
Virtual Classroom
Virtual classrooms provide all the benefits of attending a classroom course without the need to arrange travel and accomodation. Please note that virtual courses are attended in real-time, commencing on a specified date.
