Standard syllabus
Applied data science · Graduate · Data science
Topics
Advanced workflow
- Problem framing for ambiguous stakeholder goals
- Data contracts and metric definitions
- Complex wrangling across heterogeneous sources
- Causal vs predictive analysis planning
- Uncertainty quantification beyond point estimates
- Reproducible research at team scale
Experimentation and inference
- Experimental design for online and offline studies
- CUPED and variance reduction survey
- Sequential testing and peeking controls (intro)
- Observational causal methods survey (matching, DiD intro)
- Hierarchical models awareness
- Decision analysis under uncertainty
Modeling at scale
- Feature platforms and training/serving skew
- Model selection under business constraints
- Ensemble and stacking survey
- Calibration and decision thresholds
- Cost-sensitive and constrained optimization intro
- Failure modes and monitoring plans
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.