0 or 3 credits
Fall 2025 Lecture Laboratory Upper Division
Students will acquire, assess, clean, visualize and analyze biological data sets with R. Students will be able to describe the link between complex biological phenomena and the data captured through human observation or scientific instrumentation. Students will learn how to organize data sets to optimize clarity and analytic possibilities while minimizing errors with examples drawn from the literature or biological databases. R will be taught starting with small-scale data such as drug sensitivity assays moving to genome-scale analyses such as gene expression and pathway analysis later in the course. These skills will be taught in the light of increasing data literacy and enabling reproducible research through clear documentation of data and communication of analyses. Relevant concepts from biology and statistics will be reviewed.
Learning Outcomes1Understand how complex biological phenomena are captured as data.
2Learn how to manage data science projects using tools such as R, Linux and remote computing.
3Use R to acquire, assess, clean, organize, visualize and analyze biological data.
4Use R to document and communicate analysis of biological data.
Prerequisites
made by Purdue students.