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GRAPHS AND NETWORKS IN DATA SCIENCE

MATH 444
과목 설명

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.

선수과목

(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)

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.46

-1.95% 과거 데이터 대비

수료율
98.44%

-0.38% 과거 데이터 대비

A 비율
46.88%

-12.5% 과거 데이터 대비

학급 규모
64

52.38% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

COMPUTATIONAL NETWORK BIOLOGY

BMI/COMPSCI 775

Introduces networks as a powerful representation in many real-world domains including biology and biomedicine. Encompasses theory and applications of networks, also referred to as graphs, to study complex systems such as living organisms. Surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery. Enables hands-on experience in network biology by implementing computational projects.

TOPICS IN MATHEMATICAL DATA SCIENCE

ECE/MATH/STAT 888

Advanced topics in the mathematical foundations of data science

THEORY & ALGORITHMS FOR DATA SCIENCE

COMPSCI 541

Theoretical methods for data science. Topics include: review of probability background, concentration inequalities, geometry of high dimensional random variables, parametric and non-parametric estimation, selected topics from optimization (optimality conditions; deterministic and stochastic gradient descent), PAC learning, sample complexity and algorithms for linear classification and regression, and property/distribution testing. Uses Python programming language.

INTRODUCTION TO MATHEMATICAL SOCIOLOGY

SOC 375

Explores mathematical models of social structure, focusing especially on social network analysis and related methods. Makes extensive use of matrix algebra and mathematical software.

MATHEMATICAL METHODS IN DATA SCIENCE

MATH 535

A rigorous introduction to mathematical concepts important for modern data science. Topics include: matrix factorizations, optimization theory and algorithms, probabilistic models, finite Markov chains. Mathematical techniques are motivated by and illustrated on a range of applied problems from machine learning and statistics.