Analyzing Big Data with Microsoft R E-Learning

Order this unique E-Learning course Fundamentals of Microsoft R online, 1 year 24/7 access to rich interactive videos, speech, progress monitoring through reports and tests per chapter to test the knowledge directly.

The open-source programming language R has long been popular for data processing and statistical analysis. R has a concise programming language and an extensive repository of third-party libraries for performing all kinds of analysis. Microsoft R is a collection of packages, interpreters and infrastructure for developing and implementing R-based solutions for machine learning and data science. With Microsoft R, large data sets can be processed without having to load them all into memory at once. This path introduces Microsoft R, key functions and their application at a beginner to intermediate level that can benefit data scientists, analysts and statisticians in all organisations. The training course provides a description of key concepts and demonstrations.

Course content

Introduction to Microsoft R

Course: 42 Minutes

• Course Introduction
• Overview of R
• Overview of Microsoft R
• Microsoft R Products
• Introduction to Microsoft R Client
• Installing Microsoft R Client
• Integrated Development Environments (IDEs) for R
• Microsoft R Interface
• R Studio Interface
• Visual Studio for R Interface

Practice: Features of Microsoft R

Course: 2 Minutes

• Exercise: Key Features of Microsoft R

Microsoft Machine Learning (ML) Server

Course: 32 Minutes

• Overview of Microsoft Machine Learning Server
• Microsoft ML Server Components
• Operationalize Analytics
• Operationalize Analytics Using Microsoft ML Server
• Supported Platforms for Microsoft ML Server
• Microsoft ML Server in the Cloud
• Compute Context

Practice: Microsoft R Server

Course: 2 Minutes

• Exercise: List Components of Microsoft R Server

Microsoft R Packages and Functions

Course: 43 Minutes

• Course Introduction
• Introduction to R Packages
• Introduction to Microsoft R Packages
• Introduction to RevoScaleR
• RevoScaleR Data Analysis Functions
• RevoScaleR Utility and Compute Functions
• Microsoft R vs. R Functions
• Introduction to MicrosoftML
• Introduction to mrsdeploy
• Introduction to olapR
• Introduction to sqlrutils

Practice: R Packages

Course: 2 Minutes

• Exercise: Features of the RevoScaleR Package

R Data Structures and Types

Course: 21 Minutes

• Introduction to the R Language
• R Objects and Attributes
• Vectors and Factors
• Matrices and Arrays
• Data Frames and Lists

Practice: R Data Structures and Types

Course: 2 Minutes

• Exercise: R Data Frames vs. Matrices

The R Language

Course: 51 Minutes

• Course Introduction
• An Overview of The R Language
• R Functions
• Operators
• Expressions
• Control Structures
• Loops
• Subsetting Vectors and Lists
• Subsetting Matrices and Data Frames
• Subsetting Operators
• Dates and Times
• Debugging R Functions

Practice: Using the R Language

Course: 1 Minute

• Exercise: Control Structures in R

Processing Big Data

Course: 44 Minutes

• Introduction to Big Data
• Big Data Analytics
• Applications of Big Data Analytics
• Microsoft R and Big Data Analytics
• Considerations for Big Data Analysis
• Creating an XDF File
• Splitting an XDF into Multiple Files
• Chunking Algorithms

Practice: Big Data Processing

Course: 2 Minutes

• Exercise: FeaturizeText

Loading Big Data into Microsoft R

Course: 41 Minutes

• Course Introduction
• Importing Data in Microsoft R
• rxImport Function
• Importing Text Data
• Importing Multiple Files
• Importing SAS and SPSS Data
• Importing SQL Server Data
• Importing HDFS Data
• Importing ODBC Data

Practice: Loading Data into Microsoft R

Course: 2 Minutes

• Exercise: Import Text Data into Microsoft R

Data Manipulation

Course: 23 Minutes

• Introduction to Data Manipulation
• Sorting Data
• Merging Data
• Subsetting Data

Practice: Data Manipulation

Course: 2 Minutes

• Exercise: Sort and Merge Functions

Modifying and Transforming Data

Course: 20 Minutes

• Course Introduction
• Data Transformation
• Modifying Data
• Creating a Variable
• Converting Data Types

Practice: Data Modification

Course: 1 Minute

• Exercise: Using Data Modification

Predictive Analytics

Course: 33 Minutes

• Using Predictive Analytics
• Predictive Analytics Applications
• Predictive Models
• Introduction to Machine Learning
• Supervised and Unsupervised Learning
• Machine Learning Techniques
• Process of Developing Predictive Models

Practice: Predictive Data Analysis

Course: 2 Minutes

• Exercise: Analyzing and Classifying Data

Summarizing Data

Course: 35 Minutes

• Course Introduction
• Introduction to Data Summarization
• Summarizing Qualitative Data
• Summarizing Quantitative Data
• Summarizing Bivariate Relationships
• rxCrossTabs Function
• rxCube Function
• rxSummary Function
• rxQuantile Function

Practice: Data Summarization

Course: 2 Minutes

• Exercise: Using Summary Statistics Functions

Data Visualization

Course: 18 Minutes

• Introduction to Data Visualization
• Data Visualization with R
• rxHistograms
• rxLinePlots

Practice: Visualizing Data

Course: 1 Minute

• Exercise: Using Data Visualisation Functions

Linear and Nonlinear Regression Analysis

Course: 31 Minutes

• Course Introduction
• Introduction to Linear Regression
• Linear Model Accuracy Measurement
• Microsoft R and Linear Regression
• Linear Regression Interpretation
• Nonlinear Regression

Practice: Linear Regression

Course: 2 Minutes

• Exercise: Linear Regression Functions

Logistic Regression Analysis

Course: 25 Minutes

• Introduction to Logistic Regression
• Logistic Model Accuracy Measurement
• Logistic Regression Interpretation
• Microsoft R and Logistic Regression

Practice: Logistic Regression

Course: 1 Minute

• Exercise: Logistic Regression Functions

Decision Tree Analysis

Course: 31 Minutes

• Course Introduction
• Introduction to Classification Algorithms
• Microsoft R's Classification Algorithms
• Naive Bayes Classifier
• Support Vector Machines
• rxOneClassSvm

Practice: Decision Trees

Course: 1 Minute

• Exercise: One-class Support Vector Machine
• Classification Analysis

Course: 26 Minutes

• Regression Trees
• Classification Trees
• rxDTree
• Visualizing Decision Trees

Practice: Using Classification Analysis

Course: 2 Minutes

• Exercise: rxDTree Function

Clustering Analysis

Course: 20 Minutes

• Course Introduction
• Unsupervised Learning
• Introduction to Clustering Analysis
• K-means Clustering
• rxKmeans

Practice: Using Clustering Analysis

Course: 1 Minute

• Exercise: rxKmeans Function

Ensemble Learning

Course: 41 Minutes

• What Is Ensemble Learning?
• rxEnsemble
• Random Forest
• Decision Forest
• rxFastTrees
• rxBTrees
• Neural Networks

Practice: Ensemble Learning Algorithms

Course: 1 Minute

• Exercise: Model Ensembles Metrics