The two-week workshop will include both theoretical and practical components, featuring lectures on the theory of algorithms used in data science and the freeware implementation of these methods. In the first week, data sets from industry and government will be introduced by guest speakers and used as examples data sets for mini-projects. Each data set will lead to a capstone project in the second week.

Computational work will be done with Jupyter notebooks using Participants will have the opportunity to work with big data, and learn subsequent analysis motifs using machine learning, data clustering, parameter fitting, and other techniques.

There will be a short course in linear algebra and software tools prior to the workshop to cover background material in scientific computing, linear algebra, statistical methods and optimization. For more information, see the pre-workshop description.

See below for a detailed schedule. Lecture notes and workshop materials can be found on the associated GitHub repository.

Projects Overview

There will be several data sets used in the workshop. We will add in the details as they become finalized. For full project/data details, visit the projects section.

  • Data-driven modeling of video compression
    Roger Donaldson, Ph.D. (Midvale Applied Mathematics)
  • A risk-based platform for accident prevention
    Soyean Kim, P.Stat. (BC Safety Authority)
  • Project Hop: Real-time agriculutural sensing
    Nathan Vadeboncoeur (SmartSoil)
  • Elucidating enhancer-promoter gene expression using ConvNets
    Cory Simon, Ph.D. & Wouter Meuleman, Ph.D. (Altius Institute)

Recent Posts

Workshop Information

Continue reading for a full description of the workshop, a list of confirmed speakers and mentors, and a list of useful links.


Information on the material comprising the mornings of the second week. These advanced lecture topics will build on the fundamental tools developed in the first week by demonstrating important applications and generalizations of data science tools.


Join us August 16th at 6:00 PM for a panel discussion and reception, for mathematicians who are contemplating non-academic career options.


Information about the exercises in the first week associated with the morning lecture material.


Descriptions of the introductory data science material to be covered in the mornings of the first week. These introductory lecture topics will familiarize participants with domain-specific jargon and concepts, and serve as the starting point of a toolbox for the second-week projects.


Join us for a three-day mini-workshop covering background material in software carpentry, linear algebra and scientific computing.



Upcoming dates