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Data Scientist Roundup: Improving Recommendation Systems, Math for Data Science, & Pizza + Data Viz

By Emily Wilson • February 28, 2020

When our Data Scientists aren't teaching the intensive 12-week bootcamps or corporate training courses, they're working on a variety of other projects. This monthly blog series tracks and discusses some of their recent activities and accomplishments.


Vinny Senguttuvan, Sr. Data Scientist (Bootcamp)

In Vinny's latest blog post, A Case for Robust Recommendation Systems, he writes about the proliferation of recommendation systems across industries.

"As we use the internet for more and more things, knowing what a person needs, wants, and would want is not just a nice-to-have feature but almost a must-have," he writes. 

While that may lead you to believe that recommendation systems represent a mature field, that's not quite true, according to Vinny. 

"...it amazing to see how few companies get it right or make the best of it," he writes. "Amazon is a good example. Their recommendations are solely product based. If two users view the same book, they both will get the same recommendations irregardless of the fact that they both have very different browsing histories."

Read his post here, which goes on to the make the case for building more robust systems. 

Kimberly Fessel, Sr. Data Scientist (Bootcamp)

In partnership with Course Report, Metis Sr. Data Scientist Kimberly Fessel recently hosted a Math for Data Science webinar. During the 40-minute conversation, Kimberly highlights why you need math skills to be a Data Scientist and goes in-depth into which types of math you need to know in order to launch your career and find lasting success.   

"Blending coding skills with math skills is the core of data science. The algorithms that we use in data science are all worded in mathematics," said Kimberly during the webinar. "Whether it's an optimization problem, probability problem, or scoring metrics – all of those things are going to require math skills to understand what's going on. Here’s an analogy I like to use to explain this concept: in order to drive your car, you don't necessarily need to know how it all works. But if you're going to be a professional mechanic, you have to know all of those component pieces. For data science, the components are math concepts."

Watch the full webinar on the Course Report blog here, where you'll also have access to Kimberly's Google Slides and a full transcript of the conversation.

Roberto Reif, Executive Director of Data Science 

After a recent trip to a pizzeria, Roberto left with more than just a delicious dinner. He also came away with a problem to solve. In this latest blog post, What Can Ordering Pizza Teach Us About Good Visualizations?, he describes the situation: 

"My dilemma was determining if it made more sense to get two small size pizzas (8" for $9.49 each) or a medium one (12" for $16.99). I could have easily calculated the area of the circles (A = pi*rˆ2) to figure this out. However, I decided to estimate which of the options would be the best for me. Afterwards, this led me to ask if I had made the right decision? I had not!"

In the rest of the post, he details his mistake and then asks and answers the question: How does this apply to data visualization?

Read here to find out.

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See what our Sr. Data Scientists were up to last month here.


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