INFO 2950

INFO 2950

Course information provided by the Courses of Study 2018-2019.

Teaches basic mathematical methods for information science, with applications to data science. Topics include discrete probability, Bayesian methods, graph theory, power law distributions, Markov models, and hidden Markov models. Uses examples and applications from various areas of information science such as the structure of the web, genomics, social networks, natural language processing, and signal processing. Assignments require python programming.  

When Offered Spring.

Prerequisites/Corequisites Prerequisite: strong performance in an introductory statistics course from the approved list of accepted statistics courses found at and an introductory programming class with an ability to write and debug programs, or permission of instructor.

Distribution Category (MQR-AS)

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one discussion.

  • 4 Credits Graded

  • 11980 INFO 2950   LEC 001

  • Information Science majors must complete this class prior to their senior year. If you would like to enroll in this class, but are unable to, please contact the instructor at

  • 12404 INFO 2950   DIS 201

  • 12458 INFO 2950   DIS 202

  • 12459 INFO 2950   DIS 203

  • 12460 INFO 2950   DIS 204

  • 12549 INFO 2950   DIS 205

  • 12578 INFO 2950   DIS 206