R programming

r programming

CURRICULUM

  • What is business analytics
  • Applications of analytics
  • Importance of Business Analytics
  • Steps of analytics
  • Career opportunities and career path
  • Companies using R for analytics extensively
  • Role of a Data Scientist
  • Problems solved by Data Science
  • Roadmap to become Data Scientist
  • Why R
  • Installation procedure
  • R interface and using R studio
  • Setting Your Work Directory
  • Downloading new packages
  • Using R help
  • Popular websites for R reference
  • Installing Packages and Libraries in R Studio
  • Source R script
  • Overview of important R packages
  • Data Mining GUI in R
  • Graph GUI in R
  • Basic Math functions
  • Variables
  • Data structures
  • Understanding Vectors
  • Understanding data frames
  • Understanding Lists
  • Loops & Control statement
  • Factors
  • Data Values
  • Data Types : Arrays & General Array Operations
  • Data Types : Lists & General List Operations
  • Data Types : Data Frame & General Data Frame Operations
  • Data Type Conversion
  • Data Acquisition (Import & Export)
  • Subsetting Variables
  • Creating new variables
  • Renaming and Recoding Variables
  • Reshaping Data
  • Merging & Concatenating Datasets
  • Using dply to manipulate data frames
  • Control and Flow Operators
  • Make a Script in R
  • Writing Functions in R
  • Creating R package
  • Types of visualization
  • Graphs in R
  • Line Plots
  • Bar Charts & Pie Charts
  • Histograms & Density Plots
  • Scatter Plots
  • 3-D, Parallel Coordinates
  • Why study statistics
  • Applications of statistics
  • Types of statistics
  • Population vs Sample
  • Types of data
  • Types of statistical variables
  • Summarize the data
  • Make decisions using summary statistics
  • Data extraction, cleaning and doing transformations and visualization in R
  • Data extraction, cleaning and doing transformations and visualization in Python

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