FW: Data Science Student Challenge! Deadline to register Oct. 18
From: Stefi Baum <Stefi.Baum@umanitoba.camailto:Stefi.Baum@umanitoba.ca> Sent: October 10, 2019 11:09 AM To: Norm Halden <Nm.Halden@umanitoba.camailto:Nm.Halden@umanitoba.ca>; Brian Postl <Brian.Postl@umanitoba.camailto:Brian.Postl@umanitoba.ca>; Dean Agriculture <agdean@umanitoba.camailto:agdean@umanitoba.ca>; Jonathan Beddoes <Jonathan.Beddoes@umanitoba.camailto:Jonathan.Beddoes@umanitoba.ca>; Gady Jacoby <Gady.Jacoby@umanitoba.camailto:Gady.Jacoby@umanitoba.ca>; Jeff Taylor <Jeff.Taylor@umanitoba.camailto:Jeff.Taylor@umanitoba.ca> Subject: Data Science Student Challenge! Deadline to register Oct. 18
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Student Team Challenge
Prizes – Up to $1000 in cash prizes available. – Individual prizes vary depending upon the challenge. – Prizes will be awarded at the Nexus Data Science Conference: Art of the Possible, November 14, 2019https://umanitoba.us14.list-manage.com/track/click?u=a3dff152b9738c689b55dc21a&id=539f29c031&e=a0d9093abf.
Register your team today. Limited spots available. Deadline Oct. 18, 2019.
Eligibility – Fourth year undergraduate, graduate or doctorate program, U of M students. – A team of 2-3 students.
Compete in one of three team challenges:
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Use a data -driven approach to understand factors affecting product ranking Bold Commerce has an interest in gaining a deeper understanding of the competitive landscape and how their products are positioned relative to competitors. Goal Using a data-driven approach, understand factors affecting how apps are ranked across different categories.
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Using financial market data to build and test automated trading strategies Build and test futures trading strategies, assess and mitigate their risk, and test them on data. Goal Make the most money while properly mitigating risk. In other words, maximize overall profitability while minimizing the magnitude of consecutive losses (drawdowns).
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Predicting Food Preparation Time A critical aspect of food delivery is predicting food preparation time. In general, food prep time is estimated according to the cook’s experience or simply averaging historical data. Unfortunately, these predictions are inaccurate. Goal Use statistical or machine learning tools, and obtain models that perform more accurately than the restaurant guesses.
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participants (1)
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Martha Blouw