Blog

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?

By Zachariah Miller • January 30, 2018

In part 2 of Metis Sr. Data Scientist Zach Miller's exploratory series on the Monte Carlo tool, he goes over how to implement it in Python.

By Zachariah Miller • January 30, 2018

One of the most powerful techniques in any data scientist's tool belt is the Monte Carlo Simulation. It's flexible and powerful since it can be applied to almost any situation if the problem can be stated probabilistically. However, Metis Sr. Data Scientist Zach Miller has found that for many, the concept of using Monte Carlo is obscured by a fundamental misunderstanding of what it is. To address that, he's put together a series of small projects demonstrating the power of Monte Carlo in a few different fields.

By Zachariah Miller • January 18, 2018

There's no arguing it – deep learning can do some truly awesome stuff. But hear our Sr. Data Scientist Zach Miller out. When training to be an employable data scientist, these skills aren't necessary – at least not right away. Read his post to learn why.

By Jonathan Balaban • December 12, 2017

In the second installment of this series, Metis Sr. Data Scientist Jonathan Balaban provides four key strategies to unify stakeholders across your next data science consulting effort, whether you're working with a Fortune 50 firm or a small business of 50 staff members.

By Jonathan Balaban • December 05, 2017

This is part 1 of a 3-part series written by Metis Sr. Data Scientist Jonathan Balaban. In it, he distills best practices learned over a decade of consulting with dozens of organizations in the private, public, and philanthropic sectors. Enjoy part 1, and stay tuned for more in the coming weeks.

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.

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.

By Paul Burkard • May 30, 2017

This past Fall, Metis Senior Data Scientist Julia Lintern spearheaded an effort to revamp aspects of Metis’s Data Science Bootcamp curriculum in terms of both content and content delivery, which marked the first major curriculum overhaul in its 2.5-year history. This blog post is structured as some history/background on the contents of the Metis curriculum alongside an interview with Julia about the changes made.