Data Science Project Myths: Implications for Data Scientists and Management

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Details

This lecture is part of the UBC Computer Science Alumni Lecture Series. Please RSVP here: https://www.cs.ubc.ca/event/2019/09/alumniindustry-lecture-annie-ying-data-science-project-myths-implications-data-scienti

Networking starts at 6 pm, talk begins at 6:30 pm. Light refreshments will be served.

Speaker: Annie Ying, Manager, Cisco Data Science Lab, Vancouver

Abstract:

Data Science is often associated with work in big data, machine learning, AI, and statistics, but successful data science projects are rarely only due to the technical aspect of data science itself. Two things in data science projects that often get overlooked are (1) "thick data", the rich context and the why, behind the data and the problem we are trying to solve; and (2) a systematic process for engineering, evaluating, and deploying models. In this talk, I will talk about myths that data science is just about the data and the science, and the implications to data scientists and management.

Bio:

Annie is a leader in data science with over a decade of industry experience spanning corporate research and startups. Currently, she is the manager of the Cisco Data Science Lab in Vancouver. Coming from a research background with a PhD in Computer Science from McGill, Annie is a former Research Scientist at IBM T. J. Watson Research Center in New York and a two-time winner of an ACM Distinguished Paper Award (both in applying data science to software engineering) with five patents (granted and applied). Annie is active in the data science community as a Meetup organizer (Data Science for Social Good), speaker, and mentor. Growing up in Vancouver, Annie holds a B.Sc. (Honours) and a M.Sc., both from the UBC Computer Science Department and is proud be back to Vancouver two years ago.

Linked In: https://ca.linkedin.com/in/annieying