How can we train effective data scientists? Traditional lecture/lab-based courses typically involve prescribed and well-defined examples, and we found this format very effective for foundational courses that focus on a particular area of statistics, machine learning or computer programming. However, real-world data science differs greatly from these courses: data is...
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What's for dinner? Predicting customer order probabilities
by Rachel K. Riggs
One of the things that drew to me data science is its applicability to pretty much any field you can name: technology, healthcare, finance, retail, education, government, entertainment, agriculture, real estate, etc. There’s no domain too large or small and no organization that would not benefit from having a data...
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Winning the EasyMarkit AI Hackathon
by Bailey Lei
On April 6, 2019, EasyMarkit hosted their first Hackathon in Vancouver where teams were asked to offer an AI solution to improve patient communication. My team (Bailey Lei, Alex Pak, Betty Zhou) was awarded first place based on the accuracy of our model in predicting communication response from patients.
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Teaching Convolutional Neural Networks
by Mike Gelbart
When I first learned about convolutional neural networks (also known as CNNs, or convnets), I was shown a picture much like the one below, which is from the AlexNet paper:
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Designing a Master of Data Science program: goals, design decisions, and lessons learned
by Mike Gelbart
Since launching the UBC MDS program in 2016, we’ve received a lot of questions on why we designed MDS the way we did. The post will address the following design decisions:
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