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.
Wir empfehlen den Besuch unserer smart Seminare:
Nach Abschluss dieses Seminars haben die Teilnehmer Wissen zu folgenden Themen:
- 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
Dieses Seminar richtet sich an:
- 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.
Für dieses Seminar werden folgende Kenntnisse empfohlen:
- Programming experience using R, and familiarity with common R packages
- Knowledge of common statistical methods and data analysis best practices.
- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Microsoft R Server and R Client
- What is Microsoft R server
- Using Microsoft R client
- The ScaleR functions
- Exploring Big Data
- Understanding ScaleR data sources
- Reading data into an XDF object
- Summarizing data in an XDF object
- Visualizing Big Data
- Visualizing In-memory data
- Visualizing big data
- Processing Big Data
- Transforming Big Data
- Managing datasets
- Parallelizing Analysis Operations
- Using the RxLocalParallel compute context with rxExec
- Using the revoPemaR package
- Creating and Evaluating Regression Models
- Clustering Big Data
- Generating regression models and making predictions
- Creating and Evaluating Partitioning Models
- Creating partitioning models based on decision trees.
- Test partitioning models by making and comparing predictions
- Processing Big Data in SQL Server and Hadoop
- Using R in SQL Server
- Using Hadoop Map/Reduce
- Using Hadoop Spark