📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of R Programming: Basic to Advanced Course, from foundations to a certified capstone project.
Foundations
• Vectors, lists, factors and data frames, and R's vectorised idiom
• Environments, scoping and the common surprises they cause
• Projects, RStudio workflow and reproducible setup with renv
Data
• dplyr verbs and pipeline-style transformation
• tidyr reshaping and the tidy data principle
• Joins, grouping and window operations on real datasets
Visualisation
• Grammar of graphics: aesthetics, geoms, scales and facets
• Publication-quality theming and export at correct dimensions
• Choosing an encoding that answers the question honestly
Statistics
• Linear and generalised linear models and reading model output
• Mixed models for grouped and repeated-measures data
• Diagnostics, assumptions and what to do when they fail
Advanced
• Writing functions, handling errors and testing with testthat
• purrr for functional iteration and avoiding copy-paste analysis
• R Markdown and Quarto for reports that regenerate from data
Earn government-registered certification in R Programming: Basic to Advanced Course
e-Certificate and e-Marksheet issued on successful completion.
