OIT 351
AI and Data Science: Strategy, Management and Entrepreneurship
Due to space constraints, this is course is for GSB students only. Please do not email the instructors for enrollment. Thank you!
Summary:
How can one best put data science and AI to work in a modern company and manage data science teams effectively? Leaning on the emerging theory and best practices, we will examine companies at various sizes and stages, from seed through IPO, and study real-life cases to understand how companies should leverage data, data science and machine learning, build effective teams, core competencies, and competitive advantages. We will draw similarities and contrasts between regular technology and data-science-heavy companies in terms of management, technical risks, and economics, and more. The students will learn how to reason about the cost and benefits of building up a data science capability within a company, how to best manage teams to maximize performance and innovation, as well as how to evaluate the value creation through data and AI from the perspective of investors. We will have several AI entrepreneurs, executives, and investors participating in discussions.
Prerequisites: Basic fluency in Python, ML and data analysis is highly recommended, though not strictly necessary. While the class will mainly focus on the managerial and strategic aspects of AI and data science, we expect the students to have basic fluency in mathematics and quantitative reasoning.
Stanford Explore Courses: Link
Past Iterations: Spring 2023.
Associate Professor of Operations, Information & Technology
Stanford GSB
Syllabus:
Class meets Tuesdays & Fridays 1:15-2:35PM.
Class 1-2: Introduction and Fundamentals
Intro to class, DS, course overview
Class 3-4: Evaluating Product Ideas: Guidebook for Decision Making
How do we design Minimum Viable Products for AI- and ML-based products?
Guest speaker: Shir Meir Lador (Intuit)
Class 5-6: Evaluating Product Ideas: A Case-Study of AI Imaging
How do we decide whether to invest further (post MVP / in general) in a product?
How do we decide on how to expand a product?
Guest speaker: Eyal Gura (MAPS Israel, Zebra Medical), Cameron Andrews (Sirona Medical)
Class 7-8: Managing and Scaling Data Science
What are considerations for managing and scaling DS teams?
Class 9: Building Experimentation Capabilities in Your Organization
How can we build technical capabilities in a company to experiment and learn as fast as possible?
Guest speaker: Su Wang (Lyft)
Class 10-11: The Economics of Data Companies and the Investor's Perspective
How is the economics of DS/ML companies?
How defensible are their core assets?
Guest speaker: James Currier (NFX), Rob Toews (Radical Ventures)
Class 12-13: Machine Learning, Data Science and Organizational Design
What are the possible org structures and collaboration methods to maximize DS cross-functional productivity?
Guest speaker: Niva Ran (Apple), Dylan Daniels (Haus)
Class 14-15: Introduction and Cost Analysis of Generative AI
With generative AI in mind, what are the specific challenges and opportunities ahead?
Guest speakers: Santosh Raghavan (Groq), Dylan Reid (Zetta Venture)
Class 16: Frontiers of Data Science and AI
Guest speakers: Henrique Ponde (OpenAI), Shreya Rajpal (Guardrail AI), Ashwin Paranjape (Samaya AI)
Class 17-18: Course Project Demo and Final Observations
