This course will prepare students to apply quantitative techniques to the analysis of experimental data. To emphasize both practical and theoretical skills, the course will involve several hands-on workshops, and the completion of several projects will be required. Students will be well positioned to meet the emerging requirements of funding agencies for formally planned experiments and fully reproducible and documented data analysis methods.

Specific topics include: practical aspects of data formatting and management; graphical, mathematical and verbal communication of quantitative concepts; a review of statistics, with emphasis on the selection of appropriate statistical tests, the use of modern software packages, the interpretation of results, and the design of experiments; the formulation, evaluation, and analysis of mathematical models of biological function, with an emphasis on linear and non-linear regression, determination of model parameters, and the critical comparison of alternative models with regard to over-parameterization.

Prerequisites

A fair degree of competence in algebra will be needed; you should be able to solve basic algebraic equations by hand. Some prior exposure to linear algebra and calculus will be helpful.

Some computer literacy is also needed; you should be able to e-mail, surf the web, install programs on your computer, work with a spreadsheet or other analysis package to manipulate and plot data. Prior exposure to any programming language will be helpful.

This course requires access to a computer to complete. You will need to be able to install and run the R statistical package, access the internet, print, etc.

Books and Materials

Students will need a laptop computer on which they can install software (R and R Studio), and bring to class. Both packages are free, and run on recent versions of Linux, Mac OS X, and Microsoft Windows.

While the course does not require the use of a specific textbook, the following resources are recommended.

Intuitive Biostatistics, Harvey Motulsky
One of the most accessible introductions to statistics.

The Art of R Programming, Norman Matloff
One of the more comprehensive introductions to R.

R for Everyone, Jared Lander
Less in-depth than the above, but covers both basic use of R and basic statistics in a single, accessible text.

Practical Computing for Biologists, Haddock and Dunn
Covers many computing topics not covered in this class. Recommended for students considering a computational lab for a rotation or thesis.

R for Data Science, Grolemund and Wickham
Introduction to the tidyverse, focusing on importing, wrangling, exploring, and modeling your data and communicating the results. Also available online.

Teaching Assistants

There is one teaching assistant for this course: Dana Goerzen. TAs will be available during office hours. See schedule for times and locations.

Please make sure your questions are clear and complete, and that you have made an effort to find the answer yourself. The TAs are dedicated to helping you learn the concepts and skills taught in qBio, but they are not your Google fairies, and will not respond to questions that are easily answerable with a simple internet search.

Assessment

This class will be graded according to the usual WCGS scale (Honors, High Pass, Low Pass, Fail).

Grades will be determined based on two take-home problem sets, as well as a group project.

Assignments are due promptly at 11:59 PM on the due date. It is strongly suggested that you leave sufficient time to deal with technical issues (computers crashing, e-mail not working, hungry dogs, etc.) when handing in assignments.

You have up to five days of extensions/grace periods for written assignments. They must be taken in whole day increments (even if you are just two minutes late for a particular assignment). In addition to some extra time for you, this policy is also intended to provide relief from technical issues as described above; please don’t ask for any additional extensions.

All students will also be asked to complete a survey at the end of the quarter, soliciting feedback on the course to inform its content and format in future years.