CAS CS 591 - Fall 2019 - The Data Science of Electronic Commerce


Course Overview: Surprisingly, until relatively recently, the data science of electronic commerce has received relatively little attention in academia. Beginning with the advent of Internet platforms like eBay that employ online auctions, are many fascinating and innovative new markets: pay-per-click advertising markets, prediction markets, and two-sided platforms such as Uber and Airbnb. All of these application domains draw deeply on established methodologies that are highly familiar to computer scientists, notably graph theory and algorithms. However, they also build on theoretical foundations that is often unfamiliar territory to computer scientists, such as auction design, mechanism design, and the theory of matching markets. At the same time, these markets provide a unique opportunity: unlike traditional markets, many aspects of the electronic commerce marketplace are not only publicly observable, but are readily available for online measurement and data collection. Therefore, research on questions such as the prevalence of "sniping" on eBay, the effectiveness of Groupon personalizing its daily deals for subscribers, or the study of how landlords learn how to price inventory on Airbnb, can all be evaluated via large-scale measurements, enabling studies that were not previously possible.

In this class, we will consider the data science of electronic commerce from a broad and inter-disciplinary perspective, drawing primarily from insights from the Computer Science, Economics, and Marketing communities. This course is designed for students who are potentially interested in pursuing a career in or conducting research related to electronic commerce and online platforms. Our goal will be to focus on quantitative evaluation of the e-commerce marketplace, and to enable students to conduct research in this area. Please note that this course is not about entrepreneurship per se, but will provide useful background for prospective entrepreneurs. A core competency that we will develop is fluency with big data: experimental methods; best practices and techniques for data collection, data mining, and statistical analysis; effective presentation of findings; as well as the ethics of data collection. The capstone project of the course will be a research project, conducted in teams of up to three, in which students conduct a quantitative measurement-driven analysis of a computational aspect of an e-commerce firm or of consumer behavior with respect to an e-commerce marketplace.

Prerequisites: This course is designed for CS seniors who have completed all required coursework except for electives, as well as Masters students and entering Ph.D. students. While students' backgrounds will vary, it is expected that students have completed or are nearing completion of an undergraduate major in CS or in an area closely related to the course topics (such as Economics, or Marketing). Seniors who are not CS majors should seek the instructor's permission to enroll.

Instructor:  Prof. John W. Byers
Email: byers @ cs . bu . edu [preferred]
Phone: 617-353-8925 [please do not leave voice-mail; use e-mail instead]
Office Hours:
Room: MCS 101C

Open hours: Tues 10 - 11:30
By prior appointment only: Wed 2 - 3:30 [depending on the week, either in MCS 101C, or CAS 115]

Instructor Bio:

Additional Staff:  Senior Ph.D. student Harshal Chaudhari has graciously volunteered to be a resource to students in the class, later in the course. He'll be helping out to advise students on brainstorming, thinking about research directions, giving technical guidance, and to assist me with project meetings and evaluations.

Class meeting time: Tues/Thurs 2-3:15, MCS B29.

*We hope to use the Hariri Institute Conference Room, MCS 180, when available, and especially for guest speakers*

Course Requirements and Grading: There will be three components of the grade in the class: For class, we will be drawing on some material from the Easley-Kleinberg textbook (see below), but more often, we will be reading and discussing research papers. I will also be giving some lectures on technical background material for methods used in the papers. In the paper-reading portion of the course, students will be required to read and digest approximately two papers per week, prior to lecture. Students will submit short summaries and provide answers to basic questions about the papers prior to discussion. For each major topic of the course, a group of students chosen in advance will serve as specialists on the topic -- they will be experts on the papers we are discussing, and will be expected to help facilitate the discussion, brainstorm about research directions, and help with the presentation of the material (or with supplemental material).

We will have periodic short assignments, two in-class quizzes comprising short answer problems, and perhaps a few longer homework problems.

The capstone project for the course will be a semester-long research project, culminating in a writeup in the style of a conference paper, and a presentation to the class, which most likely will take the form of a poster at a class-wide poster session. The topic of the research project will be for students to conduct a quantitative measurement-driven analysis of a computational aspect of an e-commerce firm or of consumer behavior with respect to an e-commerce marketplace. Students may work alone or in teams of two, with the expected output of the teams to be commensurately larger. Suggested project topics and project deadlines will be announced after the first few weeks of the course. I will expect students in this class to take the project very seriously and there will be regular interaction with the instructor outside of class to work on the projects --- ideally, several of the projects in the class will eventually lead to publishable papers. A strong venue for Computer Science students to target could be the experimental track of the ACM Symposium on Economics and Computation. For economics students, the goal of the project would be to write a paper that could develop into a chapter of the dissertation and potentially a job market paper. Ideally, the ideas in the paper could be developed into work publishable at a top field or general interest journal.
Course Topics

Material Covered

Academic Conduct

Academic standards and the code of academic conduct are taken very seriously by our university, by the College of Arts and Sciences, and by the Department of Computer Science. Course participants must adhere to the CAS Academic Conduct Code -- please take the time to review this document if you are unfamiliar with its contents.

Collaboration Policy

The collaboration policy for this class is as follows.