INFO 2950

Introduction to Data Science

Course Description

Course information provided by the Courses of Study 2016-2017.

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.  Some assignments require python programming.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: 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, or permission of instructor.

Distribution Category (MQR-AS)

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