How to think about your career in Data science
summary
Covers a framework linking transaction-cost economics (Ronald Coase) and data patterns to concrete career choices, evaluates specific tools and patterns like DBT, Mentimeter/Benta interaction flow, and RAG/chain-of-thought plumbing; breaks one “data scientist” into ~10 roles and asks which tasks LLMs automate versus which require human judgment; discusses data gravity, complementarity, MLOps realities, and where systems get built or fail — useful if that level of tool-and-task detail decides viewing.
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chapters (7)
- 0:00Career reflection in data science
- 1:52Two decades of DS revolutions
- 10:03Evolution of data roles
- 22:49LLMs reshaping tasks
- 39:07Economic models of DS value
- 51:04Future-proofing DS career
- 1:02:02Final thoughts & Q&A
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