How to be a 10x Data Scientist
summary
Concrete practices and measurable outcomes replace vague promises: turn models and pickled weights into scalable API endpoints or containerized microservices (uploader notes a marketplace of 2,500 algorithms), prefer pre‑trained APIs when they save weeks, write small functions with clear names, add unit tests and exception handling to catch regressions, automate deployments with Ansible/Puppet/Chef, use virtualenv or Conda to pin environments, and track whether productionized models actually reduce manual work or latency.
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chapters (14)
- 0:00Introduction
- 2:5710x developer traits
- 5:03Project design for DS
- 8:33Avoiding unnecessary work in projects
- 9:41Code design best practices
- 14:21Functions and stubs
- 17:37Unit testing importance
- 18:19Right tools for the job
- 21:51Dev environment mastery
- 23:00Advanced productivity tips
- 26:34Productionizing models
- 31:58Aligning with business goals
- 33:22Python readability
- 34:06Q&A
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