Data Science and Machine Learning in Oil and Gas Dr. R. A. Leo Elworth

In part 2, we further discuss the role of the gig economy via the crowd in data science and machine learning, now and in the future.

Richard Copsey introduces the idea of looking "beyond the walls" for data science and machine learning solutions. New approaches like crowdsourcing can bring data science work in from a wide, external talent pool. We also discuss three specific oil and gas problems that were solved in part by the crowd.

In part 2, we get tips from Jenny for how to be a successful entrepreneur in the startup space of data science and machine learning. For instance, we cover the dos and don'ts of navigating a tech showcase as a startup.

Jenny Johnson discuss how to work with emerging technologies in data science and machine learning. In particular, she walks through working with startups, and more generally the startup ecosystem, to solve business pain points requiring new data science and machine learning solutions.

How does a machine learning skeptic turn into a machine learning advocate? In Part 2, Matt recounts his data science and machine learning journey from his initial introduction and skepticism to his acceptance and forward looking thoughts on where these methods will proceed from here in oil and gas.

As a senior geoscientist who has been involved in many data science and machine learning projects, Matt Morris discusses where these fields fit into the work of a geoscientist in oil and gas. He describes what problems are well suited for data science or machine learning and the qualities of the solutions that can make them more useful.