Computing Practice and Theory: Consumer Behavior

Spring 2017 quarter

Taught by

Jon Baumunk
mathematics, computer science
  • UG


Computer Science Foundations (including discrete mathematics) or equivalent experience.

This project-oriented program for intermediate and advanced computer science and business students will weave together the theory and practice of cross-cutting topics in computer science, data mining, and data analytics in the context of Big Data. The overriding question of the program is how do we organize and analyze large amounts of data to discover or confirm patterns.

The program will meet for lectures, seminar, workshops, and labs. Particularly in seminar, students will share responsibility for presenting and discussing concepts from the readings and lectures. In addition to seminar and lecture, the program will have two disciplinary components and a project. The disciplinary components will focus on: 1) data mining, machine learning, and pattern recognition and 2) statistics, modeling, and visualization.

Students will also be expected to apply the computing sub-discipline of their choice to a research paper, or a programming or statistics project, and present their work orally and in written reports. To facilitate projects, faculty will organize small affinity groups that meet twice weekly (once with a faculty adviser) to discuss progress and questions. Projects will begin with a proposal and bibliography, and should be either small enough in scope to be completed in one quarter or a self-contained part of a larger project. While faculty will encourage project work in areas related to program themes (data mining, machine learning, database systems, data visualization—especially visual analytics—networking, security, algorithmic complexity), they will approve other well-defined and promising projects that have a significant computer science or programming component. Projects can be either individual or small group.

This program aims to give students from Computability and Computer Science Foundations opportunities to continue work begun in those programs. Students who have taken Computability will be expected to complete more advanced work to earn upper-division credit.

Program Details

Fields of Study

computer science

Preparatory For

computer science and mathematics.


Spring Open

Location and Schedule

Campus Location


Time Offered


Advertised Schedule

First class meeting: Monday, April 3 at 10am (Sem II C1105)

Online Learning

Enhanced Online Learning


2017-02-16Description updated.
2016-02-08Jon Baumunk will teach this program with Richard Weiss.