Data Science and Open-Source Education for the Enterprise

Abstract: Open source tools and data science are very attractive technologies for many large enterprises. Often business leaders recognize how smaller, more agile competitors are leveraging these new technologies to disrupt aging industries, and carve out entirely new ones. The adoption of data science in key areas can often provide the analytic edge that companies need to pull ahead of competitors and innovate to drive new product and service offerings. Open source tools are often seen as a way to drive both cost savings, increase security operational efficiency, and facilitate the development of new product and service offerings.
These facts drive a desire in many large organizations to push for adoption of these new techniques and technologies company-wide, but the push for this widespread adoption is often met with resistance stemming from fear and uncertainty about how it will impact the status quo. Effective strategies for implementing enterprise-wide training and literacy campaigns for data science and open source technologies need to be provided at a level that’s accessible to the broader workforce, while at the same time hitting on the relevance of these technologies in every day work. Additionally, the vast number of freely available online learning sources and expensive boot camps and corporate training offerings can be overwhelming and can easily lead to decision paralysis.
In this talk, I’ll discuss my experience contributing to and driving data science and open source technology literacy and workforce development initiatives across multiple enterprises, both from the top-down and grassroots, bottom-up approach, leveraging a mix of internal talent, and freely available open source materials.

Bio: Zachary is currently a Lead Data Scientist at S&P Global Market Intelligence, where he leads a small team with a focus on modern natural language processing and its application to content classification and data extraction. Zachary received his PhD in Computational Physics from The College of William & Mary in 2014, where he calculated features of the strong force using simulations on high performance computing clusters. He has a passion for education, and has led and contributed to data science education initiatives at Capital One, Cloudera, and most recently at S&P Global. In his free time, he helps to organize the local Data Science Community Meetup and occasionally teaches college physics courses in Richmond, Virginia.

Open Data Science Conference