Avsnitt
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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.
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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.
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Saknas det avsnitt?
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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.
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In part two, we find out what led Dilshad to pursue science and how his career took some unexpected turns along the path to becoming a data scientist.
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Still new to being a data scientist, Dilshad tells us about his latest work on Geosteering and reveals how an afternoon at a loud cupcake cafe helped with a messy data problem.
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After discussing the topic of data science and machine learning in part 1, we dig deeper into the oil and gas industry in part 2 of our interview. In part 2, Data Engineering Manager Vincent LeMoine gives his thoughts on how data science and machine learning can play a role in oil and gas specific problems, as well as some ideas on their place in the industry as a whole.
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Data Engineering Manager Vincent LeMoine gives a fantastic introduction to the high-level topics of data science, machine learning, and oil and gas. He shares with us his take on what data science is and what skill sets are required to work in this area. After discussing the topic of data science and machine learning, we dig deeper into the oil and gas industry in part 2 of our interview.
