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Metis Blog

interviews

Working at Spotify, Transitioning from Academia to Data Science, & More – Q&A with Metis TA Kevin Mercurio

By Emily Wilson • September 05, 2017

Kevin Mercurio is a Data Scientist at Spotify and the Teaching Assistant for our upcoming Live Online Introduction to Data Science part-time course. We caught up with him recently to discuss his daily responsibilities at Spotify, his transition from academia to data science, the upcoming Metis course, and more.

interviews
Effective, Beautiful, and Fun – Mollie Pettit On the Benefits of Data Visualization

By Emily Wilson • August 24, 2017

Mollie Pettit is a big believer in the power that visualizations can wield in helping people to understand data, which can often be otherwise complex and cumbersome. Currently a full-time Data Scientist at Datascope Analytics, she'll also be teaching our upcoming part-time Data Visualization with D3.js course in Chicago. We caught up with her to discuss her passion for data viz, her recent Star Trek-focused visualization project, what work inspires her own, and more.

data science
Do You Even Data Science?

By David Ziganto • August 14, 2017

"The field is sprawling and there’s room for everyone," writes Metis Sr. Data Scientist David Ziganto. Read on for more about his view of what it takes to be a data scientist.

alumni
Demystifying Data Science: Navigating a Daily Diet of Data at Grubhub

By Metis • August 08, 2017

How does the weather affect your food-ordering patterns? Do you eat more takeout in the colder months? Do you order delivery every time a little rain hits the ground? These are the types of questions Metis bootcamp alumnus Yong Cho has been thinking a lot about lately. Now a Data Scientist at Grubhub, he's figuring out the daily impact of weather on the business.

alumni
Demystifying Data Science: Grad Goes Full Circle with Datascope Analytics

By Emily Wilson • August 02, 2017

While doing research on various bootcamps back in 2014, looking to transition out of her trade finance career, Jessica Freaner came across Metis and was drawn to the program based on the involvement of Datascope Analytics. The Chicago-based data science consulting firm was instrumental in developing the bootcamp curriculum and taught the first two cohorts in New York City, including Freaner’s. She now works there as a Data Scientist.

data science
How To Ace The Data Science Interview

By David Ziganto • July 25, 2017

There’s no way around it. Technical interviews can seem harrowing. Nowhere, I would argue, is this truer than in data science. There’s just so much to know. What if they ask about bagging or boosting or A/B testing? What about SQL or Apache Spark or maximum likelihood estimation? Sr. Data Scientist David Ziganto provides 7 must-read tips for acing your next interview.

data science
Displaying Images in Tableau (with some help from Python)

By Roberto Reif • July 24, 2017

With Tableau, one of the cool things you can do is define every pixel from an image. This allows you to create interesting visualizations as described in this blog post by Sr. Data Scientist Roberto Reif.

alumni
Demystifying Data Science: Grad Works On Cutting-Edge Self-Driving Car Technology

By Metis • July 24, 2017

Self-driving cars, once only existent in the realm of science fiction, edge closer to reality with each passing day. Data scientists like Galen Ballew, a graduate of the first Metis bootcamp in Chicago, are working day in and day out to make it so.

alumni
Demystifying Data Science: From Startup to Big Business, Being Leveraged Effectively On The Job

By Emily Wilson • July 17, 2017

This role is different for Andre Gatorano – and that’s a good thing. He’s now a Principal Data Scientist at Capital One, where he enjoys the fruits of the company’s noticeably strong commitment to a comprehensive data strategy.

data science
Faster Python - Tips & Tricks

By David Ziganto • July 14, 2017

There is a plethora of information about how to speed up Python code. Some strategies revolve around leveraging libraries like Cython, whereas others propose a “do this, not that” coding approach. There exist many “do this, not that” strategies but I decided to focus on just a few. This post is split into two parts. In Part 1, I will compare two approaches commensurate with the “do this, not that” logic to see if there is a substantial difference and, if so, which approach is better. In Part 2, I will compare Python 2 and Python 3 to see if there is credence to the claim that Python 3 is indeed faster.