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Analyzing Big Data with Microsoft R 20773 (70-773)

  • jan 21
    mandag 21/01/2019 - onsdag 23/01/2019
    09:00 - 16:00 Hillerød
    • Kr.15,895.00 ekskl. MOMS
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  • jan 21
    mandag 21/01/2019 - onsdag 23/01/2019
    09:00 - 16:00 Aarhus
    • Kr.15,895.00 ekskl. MOMS
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  • mar 18
    mandag 18/03/2019 - onsdag 20/03/2019
    09:00 - 16:00 Hillerød
    • Kr.15,895.00 ekskl. MOMS
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  • maj 20
    mandag 20/05/2019 - onsdag 22/05/2019
    09:00 - 16:00 Hillerød
    • Kr.15,895.00 ekskl. MOMS
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  • maj 20
    mandag 20/05/2019 - onsdag 22/05/2019
    09:00 - 16:00 Aarhus
    • Kr.15,895.00 ekskl. MOMS
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Kursusinfo

  • Dette kursus varer 3 dage
  • Der medfølger kursusmateriale til dette kursus
  • Dette kursus koster 4 klip på dit klippekort.
  • Fuld forplejning (Morgenmad, frokost, kage, kaffe og sodavand ad libitum)
  • Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her
  • Eksamen er inkluderet i prisen
  • Med i prisen hører Practice Test
  • Du har i alt adgang til din kursus-pc i 3 uger

Varighed

Dette kursus varer 3 dage

Materialer

Der medfølger kursusmateriale til dette kursus

Klip på klippekort

Dette kursus koster 4 klip på dit klippekort.

Forplejning

Fuld forplejning (Morgenmad, frokost, eftermiddagskage samt kaffe og sodavand ad libitum)

Garanti for beståelse

Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her

Eksamen

Alle eksamensforsøg er inkluderet i prisen

Practice Tests

Med i prisen hører Practice Test

Remote adgang

Du har i alt adgang til din kursus-pc i 3 uger

Microsoft Vouchers

5 vouchers + 4.950 kr. for Certificeringspakken.
Læs mere her

Brug det stærke programmeringssprog inden for statistik, R, på en professionel platform til big data-analyse. Microsoft R Server er en fleksibel løsning til skaleret analyse af data, udvikling af intelligente apps og registrering af værdifuld indsigt på tværs af din virksomhed.

Udvikl hurtig, prædiktiv analyse, der kombinerer den ydeevne og fleksibilitet, du har brug for, med det populære open source-R-sprog.

Få overblik over det fulde analysespektrum, fra udforskning til analyse til visualisering til modellering, og udnyt big data-statistik, prædiktiv modellering og machine learning-funktioner. R Server er fuldt ud kompatibel med R-scripts og -funktioner samt CRAN-pakker til analysering af data i enterprise-klassen.

Microsoft R Server tilbyder en fleksibel, højtydende lokal analyseløsning til dine Hadoop clusters, databaser og datalagre, der understøtter big data-statistik, prædiktiv modellering og maskinindlæringsfunktioner.

 

 

Den officielle kursusbeskrivelse fra Microsoft

About this course
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.

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

 

Course Outline

 

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

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 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

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 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

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 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

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 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

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 Models
Explain how to build and evaluate regression models generated from big dataLessons

  • 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 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

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 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

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.

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