Applied Data Science - Decision-making beyond Prediction / Fall 2023


  • 11/12 -- New Lecture is up: Lecture 16 - Differential Privacy [slides]
  • 11/05 -- New Lecture is up: Lecture 18 - Discrimination in Platforms [slides]
  • 11/05 -- New Lecture is up: Lecture 17 - Limits to prediction [slides]
  • 11/05 -- New Lecture is up: Lecture 15 - Project description and game theory [slides]
  • 10/02 -- New Lecture is up: Lecture 14 - Experimentation in marketplaces [slides]
  • 10/02 -- New Lecture is up: Lecture 13 - Experimentation -- Peeking and Interference [slides]
  • 09/26 -- New Assignment released: [Homework #3 - Dynamic and Personalized Pricing]

Course Description

This course considers the data science challenges beyond training an accurate predictive model, especially for systems about people (data of behavior), and for people (deployed models to influence behavior). Whether for online marketplaces, transportation, governmental, or urban systems, effective data science in such settings requires dealing with user incentives and strategic behavior, networked and decentralized decision-making, and privacy and ethics concerns.

Important links

Course topics

  • Data collection (~3 weeks)
    • Data constructs, surveys, ratings, polling, and implicit data exhausts
    • Challenges and biases: censoring, strategic reporting, social desirability, ratings inflation, privacy, etc
    • Technical solutions to challenges: stratification, weighting, post-processing
    • Non-technical solutions and case studies
  • Recommendations (~2 weeks)
    • Collaborative filtering and personalized recommendations; individual vs demographic based recommendations
    • Recommendations in practice: Capacity constraints, matching, 2-sided fairness, and other challenges (such as limited + missing data)
  • Algorithmic pricing (~2-3 weeks)
    • Basics of posted price mechanisms, algorithmic pricing
    • Personalized and dynamic pricing in practice (online marketplaces, supply/labor side wages, and roadway congestion pricing)
    • Fairness, ethics, and limitations
  • Experimentation (~2-3 weeks)
    • A/B testing basics
    • Experimentation in practice: networks, interference, clustering, experimentation over time, switchbacks, 2-sided experimentation, trade-offs across experiments
    • Ethics and communication of experiments
    • Introduction to causal inference without experiments
  • Miscellaneous (~2-3 weeks): Exact topics based on student interest
    • Algorithmic explainability and transparency
    • Performance drift, strategic reactions to your model, Data feedback loops
    • Human-in-the-loop machine learning
    • Fairness audits and interventions
    • Differential privacy

Previous Offerings


Teaching Assistants

Gabriel Agostini