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Big Data Analytics with Microsoft R Training

By: McTimothy Associates Consulting LLC  

Lagos State, Nigeria

03 - 05 Aug, 2022  3 days

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NGN 155,000

Venue: The Professional Place:12B Olumoroti Street Gbagada Phase 11, Lagos Nigeria

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.

Audience profile

The primary audience for this course is people who wish to analyse large datasets within a big data environment.

The secondary audience are developers who need to integrate R analyses into their solutions.

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.

At course completion

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 Scale R functions
  • Lab: Exploring Microsoft R Server and Microsoft R Client
    • Using R client in VSTR and RStudio
    • Exploring Scale R functions
    • Connecting to a remote server

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 Scale R 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

  • Explain how to visualize data by using graphs and plots.
  • Lessons
    • Visualizing In-memory data
    • Visualizing big data
  • Lab: Visualizing data
    • Using plot to create a faceted plot with overlays
    • Using rxlinePlot and rxHistogram

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

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

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

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
The Professional Place:12B Olumoroti Street Gbagada Phase 11, Lagos Nigeria Aug 03 - 05 Aug, 2022

Registration: 09:00:am - 04:00:am

NGN 155,000.00
(Convert Currency)

Blessing Aguta 09080022449, 08058805333

5% Group Discount for 3-5 participants, or 10% Group Discount for 6 and above participants

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