BIOL 5075 Introduction to Coding and Statistical Thinking for Genetics and Genomics
Work in the life sciences increasingly relies on large scale, quantitative data that requires basic computational and statistics skills. This course is an introduction to basic Python and statistical concepts used in molecular genetics and genomics, aimed at first-year DBBS students. The format emphasizes practical problem-solving skills by teaching both core statistical concepts, such as hypothesis testing, confidence intervals, bootstrap simulation, and power analysis, as well as computational methods to implement them. The goal of the course is to prepare students for more advanced coursework, as well as self-teaching during their research careers.
We are not all statisticians and computational biologists, but every biomedical scientist needs to develop and maintain a computational and statistical toolkit. Today, even scientists who spend much of their time at the bench will use and generate quantitative data, often at genome scale and single-cell resolution. We all need to read and evaluate papers that present analyses of these data. A computational and statistical toolkit is necessary to function competently as a modern biomedical scientist.
Building that toolkit is something that you will do throughout your graduate training and beyond. The purpose of this course is to get you started. You will learn some core coding and statistical skills, and most importantly, develop a foundation from which you can continue to learn as you build your toolkit throughout your career.
Instructors: Mike White and Tychele Turner, Department of Genetics
Teaching Assistants: Alyssa Erickson, Reilly Sample
Classes are held Tuesdays from 3:00 – 4:20 pm in Connor Auditorium (FLTC)