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R Programming for Data Science and Machine Learning
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R Programming for Data Science and Machine Learning

About this course

A warm welcome to the R Programming for Beginners course by Uplatz.R is a programming language regularly used for arithmetical computing and graphical demonstration to examine and visualize data. R is a software environment which is used to analyze numerical data and graphical representation. R lets us to do sectional programming using functions. R programming language was named R, founded on the first name letter of the two authors - Robert Gentleman and Ross Ihaka. An overall understanding of R should cover topics such as introduction, features, installation, RStudio ide, variables, datatypes, operators, if statement, vector, data handing, graphics, statistical modelling, etc. R not only allows us to do branching and looping but also allows to do modular programming using functions. R allows integration with the procedures written in the C, C++, .NET, Python, and FORTRAN languages to improve efficiency.R is one of the most significant instrument which is used by investigators, data analyst, statisticians, and marketers for retrieving, cleaning, analyzing, visualizing, and presenting data. R is available for Linux, MacOS, and Windows. R's binary and logical operators will look very acquainted to programmers. R can be used to compute a large variety of classical statistic tests. With R, it’s also possible to perform classification analysis such as Principal component analysis and clustering. Many types of graphs can be drawn using R, including, box plot, histogram, density curve, scatter plot, line plot, bar plot, etc.R Programming for Beginners - Course SyllabusIntroduction to R LanguageR InstallationR Data StructuresR Data TypesR PackagesSimple Calculator using RR as a CalculatorCondition Statements in RLooping in RRepeat Statement in R

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What you'll learn

  • installation of R and RStudio
  • understanding of R data types and structures
  • ability to write conditional statements and loops
  • skills to create various types of graphs
  • knowledge of statistical modeling techniques
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