Курс M20773: Analyzing Big Data with Microsoft R

Цена для физических лиц, р.: 18900
Цена для юридических лиц, р.: 19900
Цена вебинара для физических лиц, р.: 17900
Цена вебинара для юридических лиц, р.: 17900

Продолжительность курса (дней): 3

Даты (день):

Даты (вечер):

Курс готовит к тестам:

Цель:

Необходимая подготовка:

Предварительный тест:

Результат: After completing this course, students will be able to:

План курса:

20773A

• Module 1: Microsoft R Server and R Client
• Module 2: Exploring Big Data
• Module 3: Visualizing Big Data
• Module 4: Processing Big Data
• Module 5: Parallelizing Analysis Operations
• Module 6: Creating and Evaluating Regression Models
• Module 7: Creating and Evaluating Partitioning Models
• Module 8: Processing Big Data in SQL Server and Hadoop

Module 1: Microsoft R Server and R Client

• 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

• 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

• 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

• 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

• 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

• 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

Module 7: Creating and Evaluating Partitioning Models

• 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

• 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