Computing Practice and Theory: Consumer Behavior

Spring 2017
Class Size: 50
Credits per quarter

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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.

This offering will prepare you for careers and advanced study in:

computer science and mathematics.


Credits per quarter

Fields of study: 

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

Online learning:
  • Enhanced Online Learning - This offering requires access to web-based tools, but use of these tools does not displace any face-to-face instruction.

Upper division science credit:

Students seeking to earn upper-division credit must complete the "Seeking Upper Division" form during the first week of the quarter. This form will ask questions that allow students to describe the area of their proposed project work and their competency to complete work at the upper-division level (e.g., completion of two quarters of Computability). The form will be posted on the program website about mid-March.

Class Standing: Sophomore–Senior
Class Size: 50

Scheduled for: Day

Advertised schedule:

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

Located in: Olympia

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