MCSE: Data Management & Analytics Analyzing Big Data with Microsoft R
CLASS DATE(s):
Request a Class

COURSE LENGTH:

COURSE COST: $1500

COURSE TIMES: 9:00am - 4:30pm

Printable version of this course
print

COURSE OVERVIEW

This accelerated boot camp will prepare students to pass the 70-773 exam, and gives 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 AND PREREQUISITES

Candidates for this exam are data scientists or analysts who process and analyze data sets larger than memory using R. Candidates should have experience with R, familiarity with data structures, familiarity with basic programming concepts (such as control flow and scope), and familiarity with writing and debugging R functions.

In addition to their professional experience, students who attend this course should have:

  • 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.

PREREQUISITE COURSES  

*Course Cost listed does not include the cost of courseware or lunch. Course is subject to minimum enrollment. Course may run virtually as a Virtual Instructor-Led (VILT) class if minimum enrollment is not met.
Note: This course is eligibile for Microsoft SATVs.

COURSE TOPICS:


Module 1: Read and Explore Big Data
Read data with R Server
Summarize data
Visualize data

Processe Big Data
Process data with rxDataStep

Perform Complex Transforms That Use Transform Functions
Manage data sets
Process text using RML packages

Build Predictive Models With ScaleR
Estimate linear models
Build and use partitioning models
Generate predictions and residuals
Evaluate models and tuning parameters
Create additional models using RML packages

Use R Server in Different Environments
Use different compute contexts to run R Server effectively
Optimize tasks by using local compute contexts
Perform in-database analytics by using SQL Server
Implement analysis workflows in the Hadoop ecosystem and Spark
Deploy predictive models to SQL Server and Azure Machine Learning