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data science

How Biased AI Holds Data Science Back (+ Ways to Fix It) by Sr. Data Scientist Sophie Searcy

By Emily Wilson • March 06, 2018

This week, Metis Sr. Data Scientist Sophie Searcy's article, How Biased AI is Holding Us Back, and Two Things We Can Do About It, was published in InformationWeek. In it, she expounds upon ideas of increasing access for diverse groups of young people, as well as ensuring the diversification of senior leadership. Read the full article for much more insight and information on the ever-important topic.

data science
What is a Monte Carlo Simulation (Part 5)

By Zachariah Miller • March 01, 2018

This is the 5th and final installment of a series by Metis Sr. Data Scientist Zach Miller dedicated to investigating how Monte Carlo can be a great tool. In it, he discusses using Monte Carlo to measure business impact.

data science
How Business Leaders Can Keep Up With The Latest AI

By Emily Wilson • February 27, 2018

Metis Sr. Data Scientist Seth Weidman wrote an article for VentureBeat called 4 Deep Learning Breakthroughs Business Leaders Should Understand. In it, he asks a crucial question, "how can business leaders keep up with the latest AI to improve their performance?" Read his answer and find out how it ties into our Corporate Training offerings.

data science interviews
As Bootcamp Deadline Approaches, Sr. Data Scientist Discusses Value of Immersive Learning

By Emily Wilson • February 26, 2018

As we approach another Final Application Deadline (Monday, March 5th!) for Data Science Bootcamps in Seattle, San Francisco, Chicago, and New York City, we sat down with Metis Sr. Data Scientist Jonathan Balaban to ask him about the value of the 12-week program. In this Q&A, he talks about his professional background, why he wishes data science bootcamps had been around when he was learning and pursuing a career, and what students will get out of their time at Metis.

data science
What is a Monte Carlo Simulation? (Part 4)

By Zachariah Miller • February 23, 2018

In Part 4 of our ongoing series on the Monte Carlo Simulation, Metis Sr. Data Scientist Zach Miller asks and answers the question: "How do physicists use Monte Carlo to simulate particle interactions?"

data science
Video Series: Demystifying Data Science – Breaking Down Data Science Roles

By Emily Wilson • February 22, 2018

In the second installment of our Demystifying Data Science Video Series, you'll hear talks from three working data scientists about their roles, their ideas of what makes a data scientist, and what you should know about the complex, evolving, exciting field.

Made at Metis: Fighting Gerrymandering and Fighting Biased Algorithms

By Emily Wilson • February 20, 2018

In this month's edition of the Made at Metis blog series, we're highlighting two recent student projects that focus on the act of (non-physical) fighting. One aims to use data science to fight the problematic political practice of gerrymandering and another works to fight the biased algorithms that attempt to predict crime.

careers data science
Metis Bootcamp Hidden Benefits

By Metis • February 16, 2018

You may already know the basics about our bootcamp. It's intensive, lasts 12 weeks, and is project-focused, for instance. But did you know there are many hidden benefits of the bootcamp? This post will give you a better idea of everything the bootcamp has to offer.

careers data science
Video Series: Demystifying Data Science – Launching Your Data Science Career

By Emily Wilson • February 14, 2018

Back in September 2017, we hosted a free, live online conference featuring 12 straight hours of data science talks from more than 25 of the industry's best and brightest speakers. Demystifying Data Science was an awesome day of insights, advice, information, and interaction for aspiring data scientists. To share it all, we've broken the collection of recordings into a six-part blog series, of which this is the first.

data science
What is a Monte Carlo Simulation? (Part 3)

By Zachariah Miller • February 12, 2018

In Part 3 of his series, Metis Sr. Data Scientist Zach Miller tries to outsmart a casino using Monte Carlo techniques. Will it work?