Tiffany Timbers is an Assistant Professor of Teaching in the Department of Statistics and an Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. In these roles she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. One of her favourite courses she teaches is a graduate course on collaborative software development, which focuses on teaching how to create R and Python packages using modern tools and workflows.
Postdoc in Data Science Education, 2017
University of British Columbia
Postdoc in Genomics & Cell Biology, 2015
Simon Fraser University
PhD in Neuroscience, 2012
University of British Columbia
BSc in Biology, 2005
Carleton University
I regularly teach the following courses at the University of British Columbia:
I also am available to supervise STAT 548 papers. The available papers are listed below:
This book is aimed at intermediate Python users who want to package up their code to share it with their collaborators (including their future selves) and the wider Python community. It’s scope and intent is inspired by the R packages book written by Hadley Wickham and Jenny Bryan.
The goal of {canlang} is to easily share language data collected in the 2016 Canadian census. This data was retreived from the 2016 Canadian census data set using the {cancensus} R package.
rudaux sets up a course where students complete homework on a JupyterHub server that they access via a course management system (via LTI authentication), and homework is graded via nbgrader (which has both manual and autograding capabilities). Grades are posted to the course management system. In its current implementation these docs support only the Canvas course management system, but they could easily be extended to other platforms that use LTI and that have a gradebook API.
The goal of {ubccv} is to allow you to use R Markdown to create and edit your UBC Faculty CV without having to touch a word document.
An open source textbook aimed at introducing undergraduate students to data science. It was originally written for the University of British Columbia’s DSCI 100 - Introduction to Data Science course. In this book, we define data science as the study and development of reproducible, auditable processes to obtain value (i.e., insight) from data.
Using C. elegans as a model system, we used a whole-genome sequenced multi-mutation library, from the Million Mutation Project, together with the Sequence Kernel Association Test (SKAT), to rapidly screen for and identify genes associated with a phenotype of interest, namely defects in dye-filling of ciliated sensory neurons. Such anomalies in dye-filling are often associated with the disruption of cilia, organelles which in humans are implicated in sensory physiology (including vision, smell and hearing), development and disease.
Ostblom, J., Timbers, T.A. (Accepted pending minor revisions). Opinionated practices for teaching reproducibility: motivation, guided instruction and practice. Journal of Statistics and Data Science Education. Preprint available: https://arxiv.org/abs/2109.13656
Babaian, A., Drögemöller, B., Grande, B.M., Jackman, S.D., Lee, A.H., Lin, S., Loucks, C., Suarez-Gonzalez, A., Timbers, T.A. and Wright, G. (2017). hackseq: Catalyzing collaboration between biological and computational scientists via hackathon version 1; referees: awaiting peer review. F1000 Research, 6:197. doi: https://doi.org/10.12688/f1000research.10964.2
Timbers, T.A., Garland, S.J., Mohan, S., Flibotte, S., Edgley, M., Muncaster, Q., Au, V., Li-Leger, E., Rosell, F.I., Cai, J., Rademakers, S., Jansen, G., Moerman, D.G. and Leroux, M.R. (2016). Accelerating Gene Discovery by Phenotyping Whole-Genome Sequenced Multi-mutation Strains and Using the Sequence Kernel Association Test (SKAT). PLoS Genetics doi: 10.1371/journal.pgen.1006235