INFO 2950 - Introduction to Data Science

(MQR-AS)

Spring. 4 credits. Student option grading.

Prerequisite: A strong performance in an introductory statistics course from the approved list of accepted statistics courses found at http://infosci.cornell.edu/academics/degrees/ba-college-arts-sciences/degree-requirements/core-requirements and an introductory programming class with an ability to write and debug programs, or permission of instructor.

P. Ginsparg.

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.

Information Science majors must complete this class prior to their senior year.