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
Expert