Analyzing Big Data with Microsoft R E-learning
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.
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.
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.
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.
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.
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.
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.
And all for a fraction of the cost of a classroom training
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