What the...? Data Science Questions Asked and Answered
summary
A run-through of concrete claims and tradeoffs: a security company running services for 200 million Internet properties and using a lava lamp RNG to seed randomness; data science teams live in finance and deliver predictive decision-making systems that never finish because models drift and need retraining; deep learning blows budgets — yes a $40,000 AWS job happens — so teams compare cloud versus on‑prem training, add guardrails, risk‑mitigation, licensing checks for open source, and measure tolerance for bias and failure.
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chapters (7)
- 0:00Introduction
- 4:34Data science team roles
- 8:05Why data scientists code
- 13:05Can’t your model just do what we want
- 17:35AWS bill incident
- 19:18AI fear debate
- 21:44Women in tech space
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