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The R Language and Big Data Processing


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
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.

Target Audience
All individuals who wish to understand key concepts in big data analysis and Microsoft R features including scientists, analysts, and statisticians

Prerequisites
None

Expected Duration (hours)
1.6

Lesson Objectives

The R Language and Big Data Processing

  • start the course
  • recognize important characteristics of the R language
  • define R functions and list their key components
  • recognize operators in the R language
  • define expressions in R and list key characteristics of R expressions
  • list control structures in R
  • list R loops and identify their applications
  • identify important techniques for subsetting vectors and lists in R
  • recognize approaches for subsetting matrices and data frames in R
  • identify subsetting operators in R
  • recognize different classes of dates and times in R
  • recognize how to debug R functions
  • identify features of control structures
  • describe what big data is and list big data's main characteristics
  • recognize big data analytics and common techniques in analysis of big data
  • identify applications of big data in various industries
  • recognize key features of Microsoft R for analysis of big data
  • identify important considerations when analyzing big data
  • recognize what XDF file format is and identify how to create an XDF file in Microsoft R
  • identify options for splitting XDF into multiple file formats
  • recognize chunking algorithms in Microsoft R
  • identify features of FeaturizeText function
  • Course Number:
    df_abdr_a03_it_enus

    Expertise Level
    Intermediate