MOC 20773 - Analyzing Big Data with Microsoft R (Fast Track) - Feva Works IT Education Centre

MOC 20773 - Analyzing Big Data with Microsoft R (Fast Track) MS20773



本課程的主要目的是讓學生能夠使用 Microsoft R Server 在大型數據集上創建和運行分析,並展示如何在大數據環境中使用它,如Hadoop Spark 群集或 SQL服務器數據庫。

完成課程後,你將可以:

- 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 

 

本課程為微軟原裝課程,並附原裝 LAB 即時實習,由微軟認可導師 (Microsoft Certified Trainer) 教授。全個課程均為一人一機實習,理論與實戰並重。

課程附送 Digital MOC 20773 - Analyzing Big Data with Microsoft R 原裝教材 (價值 $1,500)
 

 




課程全面教授學員有關 Analyzing Big Data with Microsoft R 技術。


 

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理論: 0小時
實習: 18小時
示範: 0小時
合共: 18小時
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課程費用: $3500

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課程內容:

MOC 20773 - Analyzing Big Data with Microsoft R

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
     

 

課程時間表:

 
MS2077318120015 
日期 2018/12/13 - 2019/01/17
時間 19:00-22:00 (THU)
合共 18小時
地點 長沙灣分校
費用 $ 3500