Free Online Corporate Training Series: Intro to Python Register Now
Q&A with Greg Ryslik, Head of Data Science & Analytics at Faraday Future
By Emily Wilson • January 30, 2017
"One of the most exciting things about being a data scientist right now is that you get to see this progression. You get to see how a field evolved from its fairly early stages, all the way through to the major powerhouses within the tech world," said Greg Ryslik, instructor and designer of the upcoming Metis evening course, Statistical Foundations for Data Science and Machine Learning.
We sat down with Greg to discuss his personal journey into data science and to find out his reasons for developing this particular course.
Greg completed an undergraduate degree at Rutgers University, then went on to get his Master's in Statistics from Columbia, followed by a Ph.D. in biostatistics at Yale. His impressive resume includes time at Genentech, a major biotech company, where he did cancer biology, and Tesla Motors, where he led the data science analytics team within the service organization. He's currently the Head of Data Science and Analytics at Faraday Future.
He developed this course based on a deep understanding of how vast data science is and will continue to become. But no matter the scale, at the root of it all will be the same thing – a solid understanding of statistics and mathematics.
"Data science is a very broad field. There's a lot of automation possible in machine learning, AI, that are related algorithms, but the fact of the matter is all these algorithms rely on complex mathematics and statistics," he noted.
"While you might be able to build and deploy a basic machine learner and use it as a black box piece of software, to really understand what it's doing and to be able to optimize it for the task at hand – [along with] be able to use it to its full extent – you need a fundamental understanding of how the algorithm works, why it works, and [an understanding of] the theoretical underpinnings that make such a model go," he continued. "Having a strong knowledge of statistical mathematics...will help enable the person, not only to deploy a model, but to really optimize it and get the most impact out of it."
Watch the full interview above, and learn more about the course here. It runs from February 21st to April 13th on Monday and Wednesday evenings from 6:30 - 9:30pm.
In this post, SwitchUp interviews Brendan Herger, a Metis Sr. Data Scientist based in San Francisco. Find out what he loves about teaching, about his passion for machine learning, and how he helped found Capital One's Center for Machine Learning.
As a physicist, TV host, and our Chief Data Scientist, Debbie Berebichez is always up to something interesting. Lately, she's been focused on the relationship between Critical Thinking and Data Science and discussed it on both the DataFramed podcast and the Story By Data YouTube channel. Check out both here.
Going through a data science bootcamp is an intense experience for everyone involved. Students work at a breakneck pace unparalleled in other learning environments, absorbing new and difficult concepts and skills, and applying them to projects starting as early as week one. All the while, instructors shoulder the hopes and fears of their cohorts as they guide and teach them over the course of 12 weeks. For interested individuals, teaching a bootcamp can have tangible career benefits related to industry goals and aspirations. Read how here.