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START DATE END DATE CLASS TIMINGS MODE LOCATION ACTION
11/05/2018 11/07/2018
  • VLT
Live Online
12/03/2018 12/05/2018
  • VLT
Live Online
11/19/2018 11/21/2018
  • VLT
  • GTR
Live Online

20773: Analyzing Big Data with Microsoft R Course 

The principle motivation behind the course is to give understudies the capacity to utilize Microsoft R Server to make and run an investigation on a substantial dataset, and demonstrate to use it in Big Data situations, for example, a Hadoop or Spark bunch, or a SQL Server database. 

20773: Analyzing Big Data with Microsoft R Audience Profile 

The essential group of onlookers for this course is individuals who wish to break down substantial datasets inside a major information condition. 

The auxiliary crowd are engineers who need to incorporate R examinations into their answers. 

20773: Analyzing Big Data with Microsoft R At Course Completion

In the wake of finishing this course, understudies will have the capacity to: 

  • Clarify how Microsoft R Server and Microsoft R Client work 
  • Utilize R Client with R Server to investigate enormous information held in various information stores 
  • Picture information by utilizing charts and plots 
  • Change and clean enormous informational indexes 
  • Actualize alternatives for part investigation employments into parallel assignments 
  • Construct and assess relapse models created from huge information 
  • Make, score, and convey apportioning models produced from enormous information 
  • Utilize R in the SQL Server and Hadoop situations 

20773: Analyzing Big Data with Microsoft R Course Outline 

Module 1: Microsoft R Server and R Client 

Clarify how Microsoft R Server and Microsoft R Client work. 

Lessons 

  • What is Microsoft R server 
  • Utilizing Microsoft R customer 
  • The ScaleR capacities 

Lab : Exploring Microsoft R Server and Microsoft R Client 

  • Utilizing R customer in VSTR and RStudio 
  • Investigating ScaleR capacities 
  • Associating with a remote server 

In the wake of finishing this module, understudies will have the capacity to: 

  • Clarify the reason for R server. 
  • Associate with R server from R customer 
  • Clarify the motivation behind the ScaleR capacities. 

Module 2: Exploring Big Data 

Toward the finish of this module the understudy will have the capacity to utilize R Client with R Server to investigate huge information held in various information stores. 

Lessons 

  • Understanding ScaleR information sources 
  • Perusing information into a XDF question 
  • Condensing information in a XDF question 

Lab : Exploring Big Data 

  • Perusing a neighborhood CSV document into a XDF record 
  • Changing information on input 
  • Perusing information from SQL Server into a XDF record 
  • Producing synopses over the XDF information 

Subsequent to finishing this module, understudies will have the capacity to: 

  • Clarify ScaleR information sources 
  • Depict how to import XDF information 
  • Depict how to condense information held in XCF design 

Module 3: Visualizing Big Data 

Disclose how to picture information by utilizing charts and plots. 

Lessons 

  • Picturing In-memory information 
  • Picturing enormous information 

Lab : Visualizing information 

  • Utilizing ggplot to make a faceted plot with overlays 
  • Utilizing rxlinePlot and rxHistogram 

In the wake of finishing this module, understudies will have the capacity to: 

  • Utilize ggplot2 to picture in-memory information 
  • Utilize rxLinePlot and rxHistogram to picture huge information 

Module 4: Processing Big Data 

Disclose how to change and clean huge informational indexes. 

Lessons 

  • Changing Big Data 
  • Overseeing datasets 

Lab : Processing enormous information 

  • Changing enormous information 
  • Arranging and consolidating enormous information 
  • Interfacing with a remote server 

In the wake of finishing this module, understudies will have the capacity to: 

  • Change enormous information utilizing rxDataStep 
  • Perform sort and consolidation operations over huge informational collections 

Module 5: Parallelizing Analysis Operations 

Disclose how to execute alternatives for part examination employments into parallel undertakings. 

Lessons 

  • Utilizing the RxLocalParallel figure setting with rxExec 
  • Utilizing the revoPemaR bundle 

Lab : Using rxExec and RevoPemaR to parallelize operations 

  • Utilizing rxExec to boost asset utilize 
  • Making and utilizing a PEMA class 

In the wake of finishing this module, understudies will have the capacity to: 

  • Utilize the rxLocalParallel register setting with rxExec 
  • Utilize the RevoPemaR bundle to compose altered versatile and distributable examination. 

Module 6: Creating and Evaluating Regression Models 

Disclose how to manufacture and assess relapse models created from enormous information 

Lessons 

  • Bunching Big Data 
  • Producing relapse models and making expectations 

Lab : Creating a direct relapse show 

  • Making a group 
  • Making a relapse demonstrate 
  • Create information for making expectations 
  • Utilize the models to make expectations and analyze the outcomes 
  • Subsequent to finishing this module, understudies will have the capacity to: 
  • Bunch enormous information to decrease the extent of a dataset. 
  • Make straight and logit relapse models and utilize them to make forecasts. 

Module 7: Creating and Evaluating Partitioning Models 

Disclose how to make and score dividing models produced from huge information. 

Lessons 

  • Making parceling models in view of choice trees. 
  • Test parceling models by making and contrasting forecasts 

Lab : Creating and assessing parceling models 

  • Part the dataset 
  • Building models 
  • Running expectations and testing the outcomes 
  • Looking at comes about 

In the wake of finishing this module, understudies will have the capacity to: 

  • Make dividing models utilizing the rxDTree, rxDForest, and rxBTree calculations. 
  • Test dividing models by making and contrasting expectations. 

Module 8: Processing Big Data in SQL Server and Hadoop 

Disclose how to change and clean enormous informational collections. 

Lessons 

  • Utilizing R in SQL Server 
  • Utilizing Hadoop Map/Reduce 
  • Utilizing Hadoop Spark 

Lab : Processing huge information in SQL Server and Hadoop 

  • Making a model and foreseeing results in SQL Server 
  • Playing out an examination and plotting the outcomes utilizing Hadoop Map/Reduce 
  • Coordinating a sparklyr content into a ScaleR work process 

Subsequent to finishing this module, understudies will have the capacity to: 

  • Utilize R in the SQL Server and Hadoop conditions. 
  • Utilize ScaleR capacities with Hadoop on a Map/Reduce bunch to break down huge information.

Notwithstanding their expert experience, understudies who go to this course ought to have: 

  • Programming knowledge utilizing R, and nature with regular R bundles 
  • Learning of regular measurable techniques and information investigation best practices. 
  • Fundamental learning of the Microsoft Windows working framework and its center usefulness. 

Working information of social databases.

Awards