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Metis's Michael Galvin Talks Improving Data Literacy, Upskilling Teams, & Python's Rise with Burtch Works
By Metis • February 27, 2019
In an excellent new interview conducted by Burtch Works, our Director of Data Science Corporate Training, Michael Galvin, discusses the value of "upskilling" your team, how to improve data literacy skills across your company, and why Python is the programming language of choice for so many.
As Burtch Works puts it: "we wanted to get his thoughts on how training programs can address a variety of needs for companies, how Metis addresses both more-technical and less-technical needs, and his thoughts on the future of the upskilling trend."
In terms of Metis training approaches, here's just a small sampling of what Galvin has to say: "(One) focus of our training is working with professionals who might have a somewhat technical background, giving them more tools and techniques they can use. An example would be training analysts in Python so they can automate tasks, work with larger and more complicated datasets, or perform more sophisticated analysis. Another example would be getting them to the point where they can build initial models and proofs of concept to bring to the data science team for troubleshooting and validation. Yet another issue that we address in training is upskilling technical data scientists to manage teams and grow on their career paths. Often this can be in the form of additional technical training beyond raw coding and machine learning skills."
The word 'pioneering' is rarely associated with banks, but in a unique move, one Fortune 500 bank had the foresight to create a Machine Learning center that helped keep it from going the way of Blockbuster. Metis Sr. Data Scientist Brendan Herger was fortunate to co-found this center, and in this post, he shares some insights, particularly as they relate to successfully launching a new data science team within your organization.
Democratizing data means more than just enabling your employees to make queries on the data. It means helping them develop the skills to read graphs, think about relevant scales, and interpret what the data is saying. In this post, Sr. Data Scientist Damien Martin shares how training your team leads to better projects and happier data scientists.
Too often we assume that good data science translates to effective data science, even though it's untrue. This assumption has killed many would-be successful projects. In this post, Sr. Data Scientist Kerstin Frailey introduces the impact hypothesis, or, how to critically scope and communicate how a project will drive impact. Doing this will transform the way data science drives your business.