The primary objective of this course is to provide postgraduate students at the Master’s or PhD level with foundational knowledge of programming in R and its applications in descriptive and inferential statistical analysis for health and life sciences. The learning process will include weekly theoretical and hands-on seminars, followed by study activities and practical applications. Justification: A fundamental understanding of computer programming concepts and their application in biological data analysis is essential for students conducting research in health and life sciences. Researchers must grasp pre-analytical steps and inferential analysis methods involved in research project development, as well as the ability to clearly present results through graphical representation. Recent advances in highthroughput technologies in molecular biology and genetics, combined with increased data storage, processing, and distribution capacities, have facilitated access to large biological databases across all fields of life sciences. Basic knowledge of how to handle large-scale biological datasets is crucial for postgraduate students and researchers in health and biology. Course Content: Introduction to hardware architecture Algorithms and programming languages Introduction to SHELL Server access Database configuration Remote database access Automating tasks in GNU/Linux servers Introduction to the R programming language Descriptive analysis of structured data in R Statistical inference and graphical representation of results in R Biological data repositories Large-scale data analysis