Fundamentals of Microsoft R
This course introduces Microsoft R and its products, including installation, components, and compatibility with other network infrastructures.
Packages and Data Types
This course covers features and components of Microsoft R Server, including its installation and configuration with various databases and IT systems. It also introduces the R language, including data structures and types.
The R Language and Big Data Processing
This course covers R programming language essentials, including subsetting various data structures, scoping rules, loop functions, and debugging R functions. It also covers big data processing concepts, including chunking algorithms.
Importing and Manipulating Data
This course covers loading data into Microsoft R from various data sources, including Hadoop, ODBC, SAS, SPSS, and SQL Server. It also covers data manipulation techniques, including data sorting, merging, cleaning, and filtering.
Modifying and Summarizing Data
This course covers data manipulation, including transforming XDF files, subsetting data, modifying variables, and converting data types. It also covers data summarization, including summary statistics and data exploration using Microsoft R.
Data Visualization and Predictive Analytics
This course outlines data visualization in Microsoft R, including basic principles and R functions used to create histograms, line plots, and bar charts. It also covers predictive analytics, including modeling techniques and key algorithms.
In this course, you'll learn about various Microsoft R regression analysis models, including linear, nonlinear, and logistic.
Decision Tree and Classification Analysis
This course covers key concepts regarding decision tree analysis and classification analysis using Microsoft R. Explore functions and techniques, including rxPredict, rxDForest, rxOneClassSvm, neural networks, and support vector machines.
Cluster Analysis and Ensemble Learning
This course covers key concepts related to cluster analysis using Microsoft R and the k-means clustering technique. It also covers ensemble learning for analysis, including random forest analysis.