{"id":160,"date":"2023-06-19T17:38:22","date_gmt":"2023-06-20T00:38:22","guid":{"rendered":"https:\/\/live-usc-dornsife.pantheonsite.io\/statsms\/?page_id=160"},"modified":"2025-10-21T13:16:04","modified_gmt":"2025-10-21T20:16:04","slug":"electives","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/statsms\/electives\/","title":{"rendered":"Statistics Masters Approved Electives"},"content":{"rendered":"\n\n \n \n\n\n\n\n\n\n\n
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The following is a list of courses that can be counted toward the unit requirement for the MS in statistics given by the Mathematics department. Additional courses can be approved by faculty should they fit the necessary criteria.\u00a0 See the USC Catalogue<\/a> for course descriptions. Students should be aware that registration in any course requires that all course prerequisites be satisfied, and that no prerequisite will be waived for the special purpose of the course being an MS elective. Further below<\/a> are courses that may not<\/em> be used as electives for the Statistics Masters.<\/p>\n

BISC: Biological Sciences<\/h4>\n

BIOC\/BISC 543 (4) Human Genetics<\/p>\n

CE: Civil Engineering<\/h4>\n

CE 561 (4) Uncertainty Quantification and Data Analytics in Civil and Mechanical Engineering<\/p>\n

CSCI: Computer Science<\/h4>\n

CSCI 544 (3) Applied Natural Language Processing
\nCSCI 561 (4) Foundations of Artificial Intelligence
\nCSCI 567 (4) Machine Learning
\nCSCI 570 (4) Analysis of Algorithms
\nCSCI 585 (4) Database Systems
\nCSCI 670 (4) Advanced Analysis of Algorithms
\nCSCI 677 (4) Advanced Computer Vision
\nCSCI 686 (4) Advanced Big Data Analytics<\/p>\n

DSO: Data Sciences and Operations<\/h4>\n

DSO 528 (3) Blended Data Business Analytics for Efficient Decisions
\nDSO 530 (3) Applied Modern Statistical Learning Methods
\nDSO 536 (1.5) Monte Carlo Simulation and Decision Models
\nDSO 545 (3) Statistical Computing and Data Visualization
\nDSO 607 (3) High Dimensional Statistics and Big Data Problems<\/p>\n

ECON: Economics<\/h4>\n

ECON 513 (4) Practice of Econometrics
\nECON 610 (4) Quantitative Analysis in Macroeconomics
\nECON 612 (4) Econometric Theory
\nECON 613\/614 (4) Economic and Financial Time Series, I and II
\nECON 615 (4) Applied Econometrics<\/p>\n

EE: Electrical Engineering<\/h4>\n

EE 510 (4) Applied Linear Algebra for Engineering
\nEE 512 (3) Stochastic Processes
\nEE 550 (3) Design and Analysis of Computer Communication Networks
\nEE 553 (3) Computational Solution of Optimization problems
\nEE 559 (3) Mathematical Pattern Recognition
\nEE 562 (4) Random Processes in Engineering
\nEE 563 (4) Estimation Theory
\nEE 588 (4) Optimization for the Information and Data Sciences
\nEE 592 (3) Computational methods for Biomedical imaging
\nEE 649 (3) Stochastic Network Optimization
\nEE 660 (3) Machine Learning from Signals: Foundations and Methods<\/p>\n

FBE: Finance and Business Economics<\/h4>\n

FBE 535 (3) Applied Finance in Fixed Income Securities*
\nFBE 555 (3) Investment Analysis and Portfolio Management*
\nFBE 559 (3) Management of financial risk*<\/p>\n

*These classes have GSBA 548, Corporate Finance, as a prerequisite which cannot be waived.<\/p>\n

ISE: Industrial and Systems Engineering<\/h4>\n

ISE 520 (3) Optimization: Theory and Algorithms
\nISE 525 (3) Design of Experiments (but cannot be used to satisfy the Math 542L requirement)
\nISE 530 (3) Optimization Methods for Analytics
\nISE 538 (3) Markov Models for Performance Analysis<\/span>
\nISE 539 (3) Stochastic Elements of Simulation
\nISE 626 (3) Advanced Topics in Applied Stochastic Models<\/p>\n

INF: Informatics<\/h4>\n

INF 510 (3) Principles of Programming for Informatics
\nINF 552 (3) Machine Learning for Data Informatics
\nINF 560 (3) Data Informatics Professional Practicum<\/p>\n

MATH: Mathematics<\/h4>\n

MATH 502B (3) Numerical Analysis
\nMATH 506 (3) Stochastic Processes
\nMATH 508 (3) Filtering Theory
\nMATH 509 (3) Stochastic Differential Equations
\nMATH 512 (3) Financial Informatics and Simulation
\nMATH 530AB (3) Stochastic Calculus and Mathematical Finance
\nMATH 545 (3) Introduction to Time Series
\nMATH 547 (3) Methods of Statistical Inference
\nMATH 548 (3) Machine Learning in Quantitative Finance<\/span>
\nMATH 578AB (3) Computational Molecular Biology<\/p>\n

MKT: Marketing<\/h4>\n

MKT 512 (3) Customer Insights and Analysis
\nMKT 566 (3) Decision Making Using Marketing Analytics<\/p>\n

PM: Preventive Medicine<\/h4>\n

PM 510L (4) Principles of Biostatistics
\nPM 511ABC (4) Data Analysis
\nPM 513 (3) Statistical Methods for Analysis of Experimental Designs
\nPM 522AB (3) Introduction to the Theory of Statistics
\nPM 544 (3) Multivariate Analysis
\nPM 534 (3) Statistical Genetics
\nPM 570 (3) Statistical Methods in Human Genetics
\nPM 571 (3) Applied Logistic Regression
\nPM 603 (4) Structural Equation Modeling<\/p>\n

PSYC: Psychology<\/h4>\n

PSYC 501 (4) Statistics in Psychological Research
\nPSYC 502 (4) Analysis of Variance and Experimental Design
\nPSYC 503L (4) Regression and the General Linear Model
\nPSYC 520 (4) Test Analysis
\nPSYC 575 (4) Multivariate Analysis of Behavioral Data
\nPSYC 577 (4) Analysis of Covariance Structures
\nPSYC 578 (4) Workshop in Quantitative Methods<\/p>\n

PPD: Public Planning and Development<\/h4>\n

PPD 557 (4) Modeling and Operations Research
\nPPD 558 (4) Multivariate Statistical Analysis<\/p>\n

Quantitative and Computational Biology<\/h4>\n

QBIO 570 (4) Introduction to data structures and algorithms for science
\nQBIO 578b (3) Computational Molecular Biology<\/p>\n

SOWK: School of Social Work<\/h4>\n

SOWK 761L (3) Multiple Regression for Social Work Research<\/p>\n

SOCI: Sociology<\/h4>\n

SOCI 521L (4) Quantitative Methods and Statistics<\/p>\n

 <\/p>\n

<\/a>The following courses may not<\/em> be used as electives for the Statistics Masters:<\/strong><\/p>\n

DSO 401, 402, 510<\/address>\n
EE 464, 503<\/address>\n
FBE 529, 540, 543<\/address>\n
PPD 502X, 525, 570<\/address>\n
MATH 505A\/B or 507A\/B (other than the course used to satisfy Requirement A), 525, 532, 574, and all 600 level Math courses. Exception: Ph.D. students who are applying for MS degree and have taken 600-level Math courses such as Math 605 and Math 606 may request that these courses are counted as electives, pending the approval by the Program Director.<\/address>\n
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