These materials are designed for an elective 2-credit course in the Department of Molecular Biology and Biochemistry (MBB) at Simon Fraser University. This course is intended to accompany the lecture-based STAT 320 (Introduction to Data Science for the Life Sciences), a 2-unit course that will introduce life sciences students to data science. The examples in this lab course will be geared towards MBB students with an emphasis on molecular biology and genomic data. The expected enrollment is around 30 students.
Together, STAT 320 and MBB 343 will provide a gentle introduction to important tools in data science and will be tailored to students with no prior programming experience. The curriculum in this laboratory course will provide life sciences students with an opportunity to learn R, the most popular statistical programming language, which is commonly used for data science. During the STAT 320 lectures, students will learn theoretical and practical aspects of statistics that are directly relevant to this course. In addition, each 3-hour lab will begin with a 1-hour lecture and tutorial that will provide more specific context on the topic and data being analyzed that week as well as hands-on demonstrations of the relevant tools. These lectures will bridge the concepts taught in STAT 320 with relevant applications to biological data for students with a basic molecular biology background (MBB 222). Most weeks, students will respond to quiz questions throughout the lecture and tutorial to reinforce their understanding of the preparatory material.
Initially, we will require that STAT 320 and MBB 343 be taken concurrently. However, we will encourage life sciences departments to develop their own discipline-specific laboratory courses that could substitute for MBB 343. This approach is consistent with the model created by Statistics and Actuarial Science for the first introductory data science courses for non-Statistics majors (STAT 310 and STAT 311).
- Prerequisites: MBB 222 and one of STAT 201, STAT 203, STAT 205, or STAT 270 with a grade of at least C- or permission of the instructor.
- Corequisite: STAT 320
- In-class quizzes: 20%
- Lab assignments: 50%
- Final project: 30%
Hadley Wickham and Garrett Grolemund (2017) "R for Data Science: Import, Tidy, Transform, and Import Data”, O’Reilly.
Freely available online: https://r4ds.had.co.nz