GRAPHS AND NETWORKS IN DATA SCIENCE

MATH 444

Course Description

Mathematical foundations of networks with an emphasis on their applications in modern data science, using tools from algorithmic graph theory and linear algebra. Topics include: basics of graph theory, network statistics, graph traversal algorithms and implementation, matrix methods, community detection, PageRank, simulation of random graph models.

Prerequisties

(MATH 320 , MATH 340 , MATH 341 , or MATH 375 ) and (COMPSCI 200 , COMPSCI 220 , COMPSCI 300 , COMPSCI 310 , COMPSCI 320 , or placement in COMPSCI 300 ), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average

3.75

No change from Historical

Completion Rate

100%

No change from Historical

A Rate

75%

No change from Historical

Class Size

No change from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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