Toronto Data Workshop

A weekly hour-long discussion where industry and academic participants collectively consider, collate, share, and disseminate best practices in the critical initial data-centric steps in any data science project. Thursdays at 4-5pm (Toronto time) via Zoom. All welcome! Sign up here.

17 November 2020: If you would like to participate in the Toronto Data Workshop on Reproducibility (25/26 February 2021) then please see here.

Thanks to Hidaya Ismail for the brilliant maple leaf and dinosaur hex stickers.


Since Fall 2019, the Toronto Data Workshop (TDW) has brought together researchers from various academic disciplines as well as industry to collectively consider, collate, share, and disseminate best practices in the critical initial data-centric steps of any data science project: collection; cleaning; storage; retrieval; dissemination; and protection. We meet weekly for an hour and aim to have a roughly even split of participants from academia and industry over the course of each term.

We meet on Thursdays at 4-5pm (Toronto time) via Zoom. For an invitation please sign up here. Anyone is welcome to attend - you don’t need to be affiliated with the university. We start at 4:01 with a brief welcome and then three minutes of break-out rooms to meet some of the other participants. At 4:05 we return to the main room and the speaker is introduced. Speakers typically speak for 20-45 minutes, during which participants are encouraged to add comments and questions to the chat, rather than verbally interrupting. We then have a chaired question period, in which those with questions are asked to signal this in the chat, at which point they’ll be called on to introduce themselves and ask their question.

The current organising committee is: Faria Khandaker, Kelly Lyons, and Rohan Alexander. The TDW is a joint initiative between the Faculty of Information and the Department of Statistical Sciences at the University of Toronto and we especially thank Dean Wendy Duff and Chair Radu Craiu for their support.

Fall 2020 speakers

Date (Toronto time) Speaker Topic
Thu, 3 Sep, 4-5pm Erik Drysdale (The Hospital for Sick Children) Using hospital data
Tue, 8 Sep, 3:30-4:30pm Sophie Bennett (Industry data scientist) UK A levels algorithm issues (jointly hosted with SRI)
Thu, 10 Sep, 4-5pm A Mahfouz, Diego Mamanche Castellanos, Hidaya Ismail, Ke-Li Chiu & Paul Hodgetts (University of Toronto) Various R packages and research developed by students
Thu, 17 Sep, 4-5pm Amber Simpson (Queen’s University) Cancer and AI
Thu, 24 Sep, 4-5pm Chelsea Parlett-Pelleriti (Chapman University) Talking to non-statisticians about statistics
Thu, 1 Oct, 4-5pm Florence Vallée-Dubois (Université de Montréal) Canadian demographics by riding (1991-2015)
Thu, 8 Oct, 4-5pm Yim Register (University of Washington Data Lab) Self-advocacy within machine learning systems
Thu, 22 Oct, 4-5pm Jeff Waldman, Leanne Trimble, Leslie Barnes, & Lisa Strug (University of Toronto) Panel on data-focused resources at U of T
Thu, 29 Oct, 4-5pm Fei Chiang (McMaster University) Data currency and applications
Thu, 5 Nov, 4-5pm Andrew Whitby (Industry data scientist) Censuses
Mon, 9 Nov, 4-5pm Tom Cardoso (Globe and Mail) Bias Behind Bars
Thu, 12 Nov, 4-5pm Kevin Armstrong (University of Toronto) Measuring poverty for NGOs
Thu, 19 Nov, 4-5pm Michael Chong (University of Toronto) High-throughput Bayesian modelling workflow
Thu, 26 Nov, 4-5pm Postponed
Thu, 3 Dec, 10-11am Jenny Cresswell (World Health Organization) Underreporting and misclassification in maternal mortality data
Thu, 10 Dec, 4-5pm Shabrina Mardevi (United Nations Population Fund & University of Toronto) & Romesh Silva (United Nations Population Fund) Population data estimation
Thu, 17 Dec, 4-5pm Liza Bolton (University of Toronto), Maria Tackett (Duke University), Nathalie Moon (University of Toronto), Teon Brooks (Mozilla Firefox) Panel on teaching data-focused topics

Summer 2020 speakers

Winter 2020 speakers

Fall 2019 speakers

If you would like to receive invitations to the series, then you can subscribe here.