Course Overview

About Course

R is a pivotal tool for data analysts, statisticians, and researchers—offering robust capabilities in statistical computing, visualization, and reproducible research. This 40-hour R Programming Training delivers a comprehensive learning path—covering fundamentals, advanced workflows, and applied analytics.

Initial sessions focus on setting up R and RStudio, mastering basic syntax, and handling data structures such as vectors, lists, and data frames. These modules build a foundation in managing and manipulating data—key to effective statistical analysis  . Control flow and function constructs empower learners to write organized and reusable code.

Data import and manipulation modules offer practical skills in tidying datasets using packages like dplyr and tidyr, complemented by base R techniques . Visualization lessons use both base R graphics and the expressive ggplot2 library to create insightful charts, enhancing data storytelling skills . Statistical modules cover core methods—descriptive stats, hypothesis tests, and linear models.

Advanced topics introduce error handling, profiling, and programmatic efficiency. Reproducible reporting practices are emphasized through hands-on use of R Markdown and Quarto, fostering transparent, reproducible workflows . Learners also explore the Tidyverse ecosystem and get a primer on Bioconductor—the leading R platform for genomic data analysis

The capstone project integrates all learning elements—data import, cleaning, visualization, modeling, and report generation—culminating in a polished deliverable supported by peer review. Training is delivered via live instruction, interactive labs, and expert consults, backed by a 12-month repository of code materials and community support.

Participants complete the course well-prepared for careers in data analysis and data science, equipped to conduct rigorous analytics, generate high-quality visualizations, and produce reproducible reports. This program fosters both technical skills and professional readiness, setting learners on a path to success in data-driven roles and research environments.