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Category: data-science

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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.

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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?

data science
What is a Monte Carlo Simulation? (Part 2)

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.

data science
What is a Monte Carlo Simulation? (Part 1)

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.

data science
Dear Aspiring Data Scientists, Just Skip Deep Learning (For Now)

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.

data science
Best Practices for Applying Data Science Techniques in Consulting Engagements (Part 2): Scoping and Expectations

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.

data science
Best Practices for Applying Data Science Techniques in Consulting Engagements (Part 1): Introduction and Data Collection

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.

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.

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.

data science
Metis Approach to Data Science Education (Part 2): Staying Current in a Cutting Edge World

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.