20773A- Analyzing Big Data with Microsoft R Training

• 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.

• 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.

• 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