Data Analytics Using R

Introduction to R and its role in business analytics; installation and R environment setup (R and RStudio); basic syntax; comments, identifiers, variables, constants; operators and precedence; introduction to business data types; working with strings; overview of data-driven decision-making in business contexts; importing and exporting data in R.

Data types in R (numeric, integer, logical, character, complex); vectors and vector operations; accessing, modifying, and deleting vectors; list creation and manipulation; converting lists to vectors; matrices creation and operations; matrix arithmetic and manipulation; introduction to structured business datasets.

Arrays and multidimensional data structures; factors and categorical data handling; data frames creation and operations; accessing and modifying data frames; data sorting, merging, sub-setting; data aggregation; data cleaning basics; preparing datasets for business analytics applications.

Decision-making structures (if, if-else, nested conditions, switch); loops (for, while, repeat); loop control (break, next); introduction to functions; built-in functions (statistical, mathematical, date-time); user-defined functions; recursion basics; application of functions in business analytics tasks.

Data visualisation using R: bar charts, histograms, line graphs, pie charts; grouped and stacked charts; interpretation of visual outputs; business data storytelling; introduction to exploratory data analysis (EDA); dashboards overview; business applications of R in marketing, finance, HR, and operations analytics. Practical Exercises Data cleaning and manipulation using real-world business datasets in R. Creation of vectors, matrices, data frames and performing basic operations. Writing R functions to solve simple business analytics problems. Generating charts (bar, histogram, line, pie) and interpreting business insights. Case-based EDA of sales/HR/financial dataset using R.