MOD 20773: Analyzing Big Data with Microsoft R

Description

The purpose of this three-day course is to give students the knowledge needed to use Microsoft R Server to create and run an analysis on a large dataset, and how to utilize Microsoft R in Big Data environments. These environments are Hadoop, Spark cluster, and/or a SQL Server database.

This course is intended for IT professionals who wish to analyze large datasets within a big data environment. Additionally, IT developers who need to integrate R analyses into their solutions.

Upon completion of this course, students will be able to:

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

Prerequisites

In addition to their professional experience, it is recommended that students 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
Working knowledge of relational databases.

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

Module 1: Microsoft R Server and R Client
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
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
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
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
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
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
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
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

Enrolled