Careers in Data Science
summary
Breaks data careers into concrete job families (business analyst, BI engineer, data engineer, ML engineer, economist, research and ML specialists) and shows how to build a practical product: a security metric like a FICO score for websites using survival analysis on vulnerability lifetimes from a 50 petabyte scan backlog. Starts with a null model, iterates through roughly 50 prototypes, emphasizes reproducible cleaning and logging, uses Python/R and Tableau for EDA and dashboards, and formalizes a process called Data Science Design Thinking.
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chapters (6)
- 0:00Introduction
- 3:45What data scientists do
- 13:53Core skills for data science
- 21:14Design thinking in action
- 35:03Launching your career
- 39:45Q&A session
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