2021 Big Data & AI Case Competition
The 2021 IMI Big Data & Artificial Intelligence Case Competition for U of T Graduate Students
(U of T undergraduate students with some big data/AI experience are also welcome)
The combination of Big Data and Artificial Intelligence is reshaping the way we think, work, and live. New career opportunities are emerging and traditional ones are being transformed or eliminated. As Graduate students at one of the top universities in the world it is important to stay on top of current and emerging trends, to pursue continual personal development, and to have some fun in the process. This event is designed to achieve all three. Sponsored by the BIGDataAIHUB at the University of Toronto Mississauga (UTM), this competition is being held in collaboration with Scotiabank, STEMFellowship and ICUBE.
The 2021 IMI Big Data & Artificial Intelligence Case Competition for U of T Graduate Students is a multi-faceted competition that is open to Graduate students from any academic discipline, in any year of her or his graduate academic career at the University of Toronto. Some level of prior experience in working with big data / artificial intelligence is required.
The event is designed as a developmental opportunity for students. If selected to compete, they will gain additional hands-on exposure to big data and artificial intelligence as well as an opportunity to practice their formal presentation skills in a safe, fun and collegial, yet competitive, environment.
REGISTRATION AND COMPETITION DETAILS
Important information about the competition is as follows. Please ensure you read this thoroughly.
- Due to COVID-19 this competition is entirely virtual this year.
- Competition Timeline:
- Online applications will be accepted until 5:00 pm on Sunday, November 22nd, 2020. (See #8 below).
- Virtual Kick-Off Meeting – This information session is expected to take place via Zoom from 10:00 am – 1:00 pm on Saturday, November 28th (Details to follow). All registered competitors are expected to attend the full Kick-Off Session.
- Optional Workshops – A series of optional big data and artificial intelligence educational workshops will be scheduled in December and January for registered entrants wishing to participate. Participation is encouraged but not required.
- All teams are to submit their approach, findings and recommendations via ten-minute voice over PowerPoint presentations (i.e. Zoom, or QuickTime videos) by 5:00 pm on Saturday, February 20th.
- Team voice over PowerPoint submissions will be adjudicated by a panel of judges during the next two-week period. Five semi-finalists will be selected.
- As a condition of remaining in the competition, all semi-finalist teams selected will be required to document their approach, findings and recommendations in a written paper of up to ten pages in length, plus attachments. Some of these finalist team papers may be selected for subsequent publication. Finalist teams are to submit their written papers by 5:00 pm on Saturday, March 20th, 2021.
- The final round of presentations will be undertaking via synchronous (i.e. live) Zoom presentations on Saturday, March 27th, 2021. Finalists will present for up to 15 minutes and a 20-minute question and answer period will follow. The written submissions will be taken into consideration in the selection of the first, second and third place winners.
- To be eligible for this competition, students must be able to demonstrate that they are enrolled at the University of Toronto throughout the entire competition period. As part of the registration process, competitors are required to complete a short information profile.
- It is expected that there will be four participants per team. You will be asked to register all members of the team at the same time in the information profile portion of the registration form (#8 see below).
- Any teams initially registering fewer than four team members may expect that the competition organizers will add additional members to their team in order to top-up the complement to the target number of four members per team and to foster increased cross-disciplinary teams.
- Each team must have at least one or two members with a reasonably strong background in data analysis and / or computer science and / or programming (e.g. python).
- Diversity is encouraged. The comingling of students from different academic disciplines on each team is actively encouraged, but not a requirement.
- CLICK HERE TO REGISTER