MOC On-Demand: 20773-Analyzing Big Data with Microsoft R Course Outline
*** Note: This is an On-Demand Self Study Class, 3-days of content, 90-days unlimited access, $995 ***
You can take this class at any time; there are no set dates. It covers the same content as the 3-day instructor-led class of the same name. The cost for this MOC On-Demand class is $995. (Microsoft Enterprise customers paying with Software Assurance Vouchers, see SATV Payment note below.)
MOC On-Demand Learner Profiles
MOC On-Demand is a self-study training solution that was designed for two types of learners. First, MOC On-Demand is a great fit for experienced IT professionals who don't need a traditional 3-day class to upgrade their existing skills. They can pick and choose topics to make the most effective use of their time. Second, MOC On-Demand is perfect for highly-motivated individuals who are new to a technology and need to space their learning over a period of weeks or months. These learners can take their time and repeat sections as needed until they master the new concepts.
About MOC On-Demand
Our MOC On-Demand classes are self-study courses with 30 to 40 hours of content. They include hours of videos, hands-on labs using the actual software, and knowledge checks and were created by Microsoft to mirror the content found in the traditional live instructor-led version of this course. Those features are all part of the standard MOC On-Demand training. But don't settle for the standard MOC On-Demand class! Check out the "ONLC Extras" that you get when purchasing this course from us.
ONLC Training Centers bundles in valuable extras with our MOC On-Demand Courses. These items are not available from other training companies.
Courseware After the Course.
Get the digital courseware that is used in the live, instructor-led version of this class. While the MOC On-Demand access goes away after 90 days, you will have access to the "extra" digital courseware for an unlimited period of time.
24/7 Online Support.
You will be able to chat online with a content matter expert while you are taking your MOC On-Demand class. And, with your permission, the expert can even take over your computer to provide with assistance with your labs.
These add-ons are available exclusively by ONLC Training Centers and are offered to you at an additional cost.
Upgrade to 180-Day Access, $250.
Need more time? Extend the base 90-day access to 180 days on your MOC On-Demand class, complete with labs, videos and knowledge checks. Extension must be ordered at time of original purchase and is non-refundable.
Certification Pak, $150.
Interested in obtaining certification? Get a Transcender practice exam and a Microsoft exam voucher at this reduced price.
ILT Listener, $250.
Want to listen in and follow along with a live Instructor-Led Training (ILT) class? We offer this option for individuals on a limited budget who have time during the day to hear a live class in progress. ILT Listeners have access to their online support chat expert during the class but they do not have direct access to the live instructor.
ILT Participant, $ Varies.
You've purchased MOC On-Demand, have gone through the training and decided that you still want a live class. Just pay difference between MOC On-Demand course and and the Instructor-Led Training (ILT) class and you can have a seat in our live class. Get both self-study and live, instructor-led training for the retail price of the instructor-led class alone!
Paying with Software Assurance Training Vouchers (SATV)
For Microsoft Enterprise customers paying with Software Assurance Vouchers, the cost of this class is 5 vouchers--this includes access to the self-study materials, the student workbook, and 24/7 access to an online expert.
Do You Still Prefer a Live, Instructor-led Class?
Already know MOC On-Demand is not right for you? We also offer this same course content in a live, instructor-led format. For more details, click on the link below:
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.
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.
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.
At course completion
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
Module 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.
• 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.
• 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.
• 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.
• 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.
• 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
• 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.
• 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.
• 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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