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MOC-20773 Analyzing Big Data with Microsoft R

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.

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

Täpsema kirjelduse leiad: https://www.microsoft.com/en-us/learning/course.aspx?cid=20773

Course Outline:

  • Module 1: Microsoft R Server and R ClientExplain 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
    • After completing this module, students will be able to:
      • Explain the purpose of R server.
      • Connect to R server from R client
      • Explain the purpose of the ScaleR functions.
  • Module 2: Exploring Big DataAt 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
    • After completing this module, students will be able to:
      • Explain ScaleR data sources
      • Describe how to import XDF data
      • Describe how to summarize data held in XCF format
  • Module 3: Visualizing Big DataExplain 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
    • After completing this module, students will be able to:
      • Use ggplot2 to visualize in-memory data
      • Use rxLinePlot and rxHistogram to visualize big data
  • Module 4: Processing Big DataExplain 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
    • After completing this module, students will be able to:
      • Transform big data using rxDataStep
      • Perform sort and merge operations over big data sets
  • Module 5: Parallelizing Analysis OperationsExplain 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
    • After completing this module, students will be able to:
      • Use the rxLocalParallel compute context with rxExec
      • Use the RevoPemaR package to write customized scalable and distributable analytics.
  • Module 6: Creating and Evaluating Regression ModelsExplain 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
    • After completing this module, students will be able to:
      • Cluster big data to reduce the size of a dataset.
      • Create linear and logit regression models and use them to make predictions.
  • Module 7: Creating and Evaluating Partitioning ModelsExplain 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
    • After completing this module, students will be able to:
      • Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
      • Test partitioning models by making and comparing predictions.
  • Module 8: Processing Big Data in SQL Server and HadoopExplain 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
    • After completing this module, students will be able to:
      • Use R in the SQL Server and Hadoop environments.
      • Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyze big data

Koolitaja:
Henn Sarv
Henn koolitab IT Koolituses juba üle 20 aasta. Koolituskogemus oli tal ka varem – jagas paar aastat teadmisi Novelli administraatoritele. IT Koolituses on ta keskendunud peamiselt (aga mitte ainult) Microsofti toodetele, seda nii tavakasutajatele (Excelist Visioni) kuni süsteemiadministraatorite ja programmeerijateni. Peamiselt siiski kõik, mis puudutab andmebaase (SQL) ja ärianalüüsi (BI, Excel).
Hoolimata sellest, et Henn on 20+ aastat klassi ees seisnud, ei ole ta kaotanud sidet igapäevase eluga: vahepeal on ta loonud arendusettevõtte ja jõudnud selle maha müüa; on konsulteerinud ja juurutanud süsteeme mitmes ettevõttes, peab talu ja korraldab sulgpallivõistlusi. Pidev side tegeliku eluga on see, mis aitab Hennul olla ekspert ja usaldusväärne allikas ka teadmiste jagamisel. Hennu enda õppimisvõime ja üle 60 sooritatud erialaeksami võimaldab õppijaid suurepäraselt mõista ning on tugevuseks ka koolitajana.
Hennul on vist peaaegu kõik sertifikaadid, mida üldse võib hankida. Vähemalt Microsofti (kuigi mitte ainult) omad ja vähemalt oma valdkondades. Oli aeg, kus Hennul tõesti olid KÕIK Microsofti sertifikaadid. MCP, MCT, MCDBA, MCSE, MCSA, MCTS, MCITP – kõik ei tule meeldegi. Lisaks on Henn juba 10 aastat ka Microsofti MVP – enimväärtuslik proff.

  • Kõrgharidus (TTÜ majandusinformaatika erialal)
  • Microsoft Certified Trainer
  • MS Certified Systems Engineer
  • Microsoft Certified Professional Developer
  • Certified Novell Administrator
  • Certified Lotus Specialist
  • MS Sales Specialist
  • Aasta koolitaja 2011 (Andras)

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MOC-20773 Analyzing Big Data with Microsoft R

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