MOC 20773 Analyzing Big Data with Microsoft R

Popis kurzu

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.

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.

Určeno pro

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.

Obsah kurzu

After completing 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 

Course Outline

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. 

Předpoklady

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.

Studijní materiály