Analyzing Big Data with Microsoft R- Course 20773 Classroom Live Eagan, MN October 08, 2018

Course Overview

• Module 1: Microsoft R Server and R Client
• Explain how Microsoft R Server and Microsoft R Client work.
  • 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
  • After completing this module, students will be able to:
    • Explain the purpose of R server.
    • Connect to R server from R client
    • Explain the purpose of the ScaleR functions.
• Module 2: Exploring Big Data
• 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.
  • 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
  • After completing this module, students will be able to:
    • Explain ScaleR data sources
    • Describe how to import XDF data
    • Describe how to summarize data held in XCF format
• Module 3: Visualizing Big Data
• Explain how to visualize data by using graphs and plots.
  • Lessons
    • Visualizing In-memory data
    • Visualizing big data
  • Lab : Visualizing data
    • Using ggplot to create a faceted plot with overlays
    • Using rxlinePlot and rxHistogram
  • After completing this module, students will be able to:
    • Use ggplot2 to visualize in-memory data
    • Use rxLinePlot and rxHistogram to visualize big data
• Module 4: Processing Big Data
• Explain how to transform and clean big data sets.
  • Lessons
    • Transforming Big Data
    • Managing datasets
  • Lab : Processing big data
    • Transforming big data
    • Sorting and merging big data
    • Connecting to a remote server
  • After completing this module, students will be able to:
    • Transform big data using rxDataStep
    • Perform sort and merge operations over big data sets
• Module 5: Parallelizing Analysis Operations
• Explain how to implement options for splitting analysis jobs into parallel tasks.
  • 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
  • After completing this module, students will be able to:
    • Use the rxLocalParallel compute context with rxExec
    • Use the RevoPemaR package to write customized scalable and distributable analytics.
• Module 6: Creating and Evaluating Regression Models
• Explain how to build and evaluate regression models generated from big data
  • 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
  • After completing this module, students will be able to:
    • Cluster big data to reduce the size of a dataset.
    • Create linear and logit regression models and use them to make predictions.
• Module 7: Creating and Evaluating Partitioning Models
• Explain how to create and score partitioning models generated from big data.
  • 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
  • After completing this module, students will be able to:
    • Create partitioning models using the rxDTree, rxDForest, and rxBTree algorithms.
    • Test partitioning models by making and comparing predictions.
• Module 8: Processing Big Data in SQL Server and Hadoop
• Explain how to transform and clean big data sets.
  • 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
  • After completing this module, students will be able to:
    • Use R in the SQL Server and Hadoop environments.
    • Use ScaleR functions with Hadoop on a Map/Reduce cluster to analyze big data.

Prerequisites

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.

Working knowledge of relational databases.