Value of Data and AI

Course overview

Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should practitioners understand and exploit the AI service market to obtain accurate and cheap predictions? How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? In this graduate seminar, we will investigate these questions by studying recent research on these topics and by hosting in-depth discussions with experts from industry and academia. Key topics will include value of data quantity and quality in statistics and AI, AI service marketplaces, business models, social good and justice around data, economic theory, regulation around data, and emerging data protection regulations. We will have guest speakers from NASDAQ, Facebook, etc to talk about industrial view of data and AI. Students will also conduct a hands-on research project in group.

Location: Wednesdays and Fridays at 10-11:20, via Zoom. This course is zoom only and there will not be any in-person meeting.


This course will require sufficient mathematical maturity to follow the technical content; some familiarity with data mining and machine learning and at least an undergraduate course in statistics are recommended. However, the course will be accessible to a wide audience including graduate students in computer science, engineering, economics, law and business.


Matei ZahariaLinks to an external site. (

James Zou (Links to an external site.) (

Steve EglashLinks to an external site. (


Lingjiao Chen (Links to an external site.) (

Piazza sign-up link: (Links to an external site.)

Scribes should be forwarded to the TA within one week after each lecture:

Office hours

The instructors are all available to meet with you.  Please email the instructors if you'd like to meet. 


Course project (65%): The main assignment is a quarter-long project, which could range from original research to a case study of a particular company or industry. We will ask students to form small groups and submit a project proposal in the first two weeks of the course, and we will then meet with each group to gauge their progress and provide advice. Each group will do interim and final presentations. Each group will also submit a final report (up to 8 pages).

Class participation (20%): every student should read the assigned papers and actively engage in class discussions.

Class scribing (15%): students will be responsible for scribing one class. Good scribing should supplement the class discussion with additional readings. 


Date Topic

Readings (required in red


Introduction, examples of data and business models

(Lecture slides,Lecture Note)

  1. AI and economics .
  2. AWS data exchange .
  3. FlatIron cancer data 

Business value of data, Netflix case study

(Lecture slides, Lecture Note)

  1. Netflix recommender system.
  2. Data inverting

Guest Talk on NASDAQ Data Products: Brad Peterson (NASDAQ CTO/CIO), Bill Dague (Head of Alternative Data) and Mike O'Rourke (SVP, Head of AI)

(Lecture slides, Lecture Note)



Case studies of ML applications and ways to evaluate ML impact (Lecture Note)

  1. Bandito
  2. Workforce Applications

Guest speaker: Susan Athey (Stanford GSB), "The Value of Data for Personalization" (Lecture slides, Lecture Note)


Design of data platforms, Databricks case study

(Lecture slides, Lecture Note)

  1. Evolution of decision support systems (up to page 18) (Ch.1 of Building the Data Warehouse)
  2. How to build an analytics team for impact in an organization

Data valuation (Lecture Note)

1.HAI article on data valuation

2. Data Shapley. 


Data and AI for energy and sustainability (Lecture slides, Lecture Note)



2. Rohit Nishant et al., Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda




ML as a service market (Lecture Note)

  1.  FrugalML
  2. Competition over data


Student project interim presentations



Machine learning in production

(Lecture slides, Lecture Note)

  1. Hidden Technical Debt in Machine Learning Systems
  2. Data Validation for Machine Learning


Guest Speaker: David Engstrom (Stanford Law School)(Lecture Note)



Privacy and security

(Lecture slides, Lecture Note)

  1. NYTimes cell phone tracking 
  2. California data brokers registry (from CCPA).
  3. Robust De-anonymization of Large Sparse Datasets.


ML accountability and fairness (Lecture Note)

1. Designing fair AI. 


Business Models & Scaling Effects (Lecture Note)

  1. WeWork: Blitzscaling or Blitzflailing? (Links to an external site.)
  2. Reid Hoffman Shares Lessons (Links to an external site.)
  3. The fundamental problem with Silicon's Valley's favorite growth strategy (Links to an external site.)
  4. Response to The fundamental problem...


Regulation of AI and data

(Lecture slides, Lecture Note)

  1. GDPR.
  2. California Consumer Privacy Act.


Guest speaker: Vladimir Federov (Facebook)



VC discussion ()Lecture Note)



 No formal lecture (office hour for final projects)



 Student project final presentations



Course Summary:

Date Details