Data science interview sprint
Statistics, ML, experimentation, and impact storytelling — interview-ready in 30 days.
Universal data science interview prep: probability, SQL, feature engineering, supervised/unsupervised ML, A/B testing, and production ML basics. Pairs with the Data engineering sprint and optional Data fundamentals elective.
Who this is for: Data scientists, ML engineers, and analysts moving into product DS roles.
outcomes
- Explain ML metrics, bias-variance, and model selection trade-offs
- Design experiments and interpret A/B test results without p-hacking
- Communicate business impact with clear metrics and stakeholder stories
related roadmaps
Regional electives, shared foundations, and sibling sprints — pick what matches your target role.
lessons
- 1 Week 0: Your 30-day battle plan Universal schedule, pass gates, and how to pick a regional elective.
- 2 Week 1: Foundations interview bar Baseline every loop tests before depth topics.
- 3 Week 1 checkpoint Self-audit before Week 2 depth.
- 4 Week 2: Statistics & probability Distributions, bias-variance.
- 5 Week 2: Machine learning fundamentals Metrics, baselines, leakage.
- 6 Week 3: Experimentation & causality A/B tests, guardrails.
- 7 Week 3: ML in production Features, serving, drift.
- 8 Week 3: Case studies & communication Business impact storytelling.
- 9 Week 4: Behavioral prep STAR stories that work in any culture — tune examples in your elective.
- 10 Week 4: Mock drills & interview day Timed technical + portfolio + behavioral practice.
- 11 Complete pass checklist Final audit before booking interviews.