20773A- Analyzing Big Data with Microsoft R Training

Analyzing Big Data with Microsoft R Training offered by Multisoft Systems gives the thorough understanding of the Microsoft R Server. The main objective of this course is to provide learners with the ability to utilize the Microsoft R Server to design and run the analysis process on the larger datasets, and show the best possible ways of utilizing it in the Big Data settings such as Spark Cluster, Hadoop, or SQL Server Database, etc.

After completing the Analyzing Big Data with Microsoft R Certification Training, you will learn to:

• How to use the Microsoft  R Server to read, process, and investigate the larger datasets.
• Read the data from the general files into R’s data frame object, explore the data structure and make needful corrections, and store the prepared data sets for the future uses.
• Make and change the data.
• Evaluate important summary of statistics, write down your very own summary, and create the data with ggplot2 packages.
• Create analytical models, calculate and compare the models, and make predictions for the new data.

Target Audience

• Candidates who wish to analyze the large datasets within the Big Data environment can undergo this training.
• This Course is perfect for the developers who want to integrate R analyses into their solutions.

Prerequisites

To enroll in this course, candidates must possess the following perquisites:

• Understanding of common statical approaches and data analysis.
• Basic knowledge of Microsoft Windows OS and its key functionalities.
• Understanding of Relational databases
• Work experience in Programming using R and acquaintance with the common R packages

Module 1: Microsoft R Server and R Client – An overview of how Microsoft R Server and Microsoft R Client work.

• What is Microsoft R Server?
• Using Microsoft R client
• The Scale R functions

Module 2: Exploring Big Data - At the end of this module the student will be able to use the R Client with R Server to explore the big data held in the different data stores.

• Understanding Scale R data sources
• Reading data into an XDF object
• Summarizing data in an XDF object

Module 3: Visualizing Big Data- An introduction to how to visualize data by using graphs and plots.

• Visualizing In-memory data
• Visualizing big data

Module 4: Processing Big Data – An explanation of how to transform and clean big data sets.

• Transforming Big Data
• Managing datasets

Module 5: Parallelizing Analysis Operations - An explanation of how to implement options for splitting analysis jobs into parallel tasks.

• Using the RxLocalParallel compute context with rxExec
• Using the revoPema R package

Module 6: Creating and Evaluating Regression Models – A brief introduction to how to build and evaluate regression models generated from big data.

• Clustering Big Data
• Generating regression models and making predictions

Module 7: Creating and Evaluating Partitioning Models - An explanation of how to create and score partitioning models generated from big data.

• Creating partitioning models based on decision trees.
• Test partitioning models by making and comparing predictions

Module 8: Processing Big Data in SQL Server and Hadoop – An overview of how to transform and clean big data sets.

• Using R in SQL Server
• Using Hadoop Map/Reduce
• Using Hadoop Spark