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

Description of the course

This two-quarter 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.


  • Peter Ralph, main instructor:
    • office hours: Monday 12:30-1:30pm or by appointment (location TBD)
  • Aidan Short, graduate teaching assistant:
    • office hours: Wednesday 9-11am (location TBD)
  • Bill Cresko, supporting instructor:

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.


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.



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

It’s our goal to run this class in a way that provides everyone access in a way that feels safe, flexible, and supportive. As of the start of the quarter, UO requires students and employees to be fully vaccinated, and masks to be properly worn inside. We expect everyone to follow these requirements. We plan to teach class wearing masks, but if you rely on lip reading to understand lectures, please let me know, and I’ll figure something out. For more information about UO’s current policy, see this Q&A. And, here is the UO’s COVID containment plan for classes. In short, you should not come in if you have symptoms, and if you test positive or have come in close contact with a case you should fill out the contact reporting form.

However, there will be times when you can’t or don’t want to attend class in person: for instance, if you have COVID syptoms, if you are a member of a high-risk group, or if life intervenes in some other way. So, we will make class available remotely as well, by broadcasting lectures on zoom (see Canvas for the link) and making the recordings available afterwards on Canvas. We’ll do our best to make participation possible for remote students, and will discuss ways to facilitate this in class. If something is not working for you, please tell us: we are committed to making the course accessible to everyone, and if it’s a problem for you, it’s almost certainly a problem for others as well.

We recognize that it is still an extremely challenging time for many people. It certainly has been for us, given the ongoing pandemic, failure to address systemic racism, inaction on climate change, wildfires and other effects of climate change, elections increasingly affected by disinformation, and ongoing attacks on women’s equality and autonomy. However: central to many (all?) of these issues are information, science, and data. One aim of this class is to empower you to engage meaningfully: to evaluate disparate sources of information, and to process and communicate lessons from data for a broader audience. That said, it is still a challenging time. If you need flexibility on assignments or projects, or even just someone to talk to, please let us know.

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

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).