See the schedule for a list of topics by week, with links to slides.

Description of the course

This graduate course aims to provide students with practical understanding of and experience with core concepts and methods in modern data analysis. The focus is on biological data, but skills will be transferable to other disciplines. Students will become familiar with major topics in univariate and multivariate statistics, analysis of large data sets, and Bayesian analysis. There is a particular emphasis on modeling and conceptual understanding of statistical noise and uncertainty. The course is advanced in that we will move through the material quickly with the goal of providing a solid foundation for subsequent learning. Students will learn to use the powerful statistical programming language R, and the flexible modeling package Stan.

Instructors:

• Peter Ralph, main instructor: plr@uoregon.edu
  • office hours: Tuesday 3-4:30pm in 275 Onyx or by appointment
• Ananya Kapoor, graduate teaching assistant: akapoor@uoregon.edu
  • office hours: Wednesday 10am-noon (471 MCK)
• Bill Cresko, supporting instructor: wcresko@uoregon.edu

Course Information

• Schedule: Tue/Thur 10:00-11:50 AM in B040 PSC and on zoom (see Canvas for link)
• Websites: besides the page you are looking at, assignments and announcements will be distributed on the Bi 610 canvas page
• Assignments: Assigned, and due, on Thursdays.

Textbooks

We will be assigning reading from these books:

• Quinn, G. & M. Keough. 2002. Experimental Design and Data Analysis for Biologists. Cambridge Univ. Press.

• Kruschke, J. 2018. Doing Bayesian Data Analysis, 2nd ed. Academic Press. (website with data and code)

There is a list of other useful reading materials on the page of references.

Software:

• Latest version of R (install here)
• A working version of RStan
• An R scripting environment such as RStudio,
• or a good code editor like Atom or BBEdit or vim
• optional: Git (here is an intro)

Prerequisites:

Students should be comfortable with algebra and the basics of calculus, have some familiarity of introductory statistics, and have some experience with programming.

Class structure, remote learning, and support

I aim to make class available remotely, by broadcasting lectures on zoom (see Canvas for the link) and making the recordings available afterwards on Canvas. However, for most students in-class participation is more engaging and results in better learning outcomes, so I strongly encourage in-person attendance. However, I know that things happen in life that might make you unable to come – for instance, please do not come to class if you are sick. (Also note that UO currently requires students and employees to be fully vaccinated.)

If you are unable to turn in assignments on time, please let me know when you are able, so I can make alternative arrangements.

Assignments and evaluation

Course grades will be based entirely on assignments. Most assignments are weekly homeworks, assigned on Thursdays and due the following Thursday. These will be short, readable, descriptive statistical reports, which you will write in Rmarkdown (here are some tips for doing this), and so also contain the R code that produces the analyses and figures. Once or twice during the quarter instead of a report we will also assign peer reviews, where you review a classmate’s assignment (here’s how to do this). There will also be a few other types of assignment: for instance, filling out a class survey (graded on participation only), and (in Winter quarter) a group data analysis project. The contribution of these to the final grade will be proportional to the points assigned, and thresholds for letter grades will be chosen to reflect a graduate-level course: A: good, demonstrates competance in all topics and mastery of many; B: acceptible, demonstrates competance in all topics; C: unsatisfactory.

Inclusion and accessibility

Please tell us your pronouns and/or name, especially if it differs from the class roster. We take seriously our responsibility to create inclusive learning environments. Please notify us if there are aspects of the instruction or design of this course that result in barriers to your participation! You are also encouraged to contact the Accessible Education Center in 164 Oregon Hall at 541-346-1155 or uoaec@uoregon.edu.

We are committed to making our classroom an inclusive and respectful learning space. Being respectful includes using pronouns for your classmates. Your classmates come from a diverse set of backgrounds and experiences; please avoid assumptions or stereotypes, and aim for inclusivity. Let us know if there are classroom dynamics that impede your (or someone else’s) full engagement.

Please see this page for more information on campus resources, academic integrity, discrimination, and harassment (and reporting of it).