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.
After completing this course, students will be able to:
In addition to their professional experience, students who attend this course should have:
It is recommended that delegates review this self-pace content to gain an introduction to the R language
Explain how Microsoft R Server and Microsoft R Client work.
After completing module1, students will be able to:
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.
After completing module 2, students will be able to:
Explain how to visualize data by using graphs and plots.
After completing module 3, students will be able to:
Explain how to transform and clean big data sets.
After completing module 4, students will be able to:
Explain how to implement options for splitting analysis jobs into parallel tasks.
After completing module 5, students will be able to:
Explain how to build and evaluate regression models generated from big data
After completing module 6, students will be able to:
Explain how to create and score partitioning models generated from big data.
After completing module 7, students will be able to:
After completing module 8, students will be able to:
If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.
Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.