Biostatistics (MS)

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The MS in Biostatistics degree program consists of at least 39 units from courses in Biostatistics and Epidemiology and will prepare students for a professional or academic career in biostatistical and biomedical research. Students will learn the foundations and theory behind biostatistics; how to apply statistical methods to draw conclusions from the data; to program in various statistical software packages; adapt existing methodologies to use in emerging fields such as clinical trials or genetics and publish a body of independent research. This program may be used as a foundation for a PhD in Biostatistics, Statistics, Epidemiology or Data Science.

The department encourages applicants with undergraduate degrees in mathematics, statistics or biostatistics, or other related fields. Applicants are encouraged to have preparation in differential and integral calculus, introduction to mathematical statistics and basic computer programming.

Admissions requirements can be found on the Department of Population and Public Health Sciences website: https://pphs.usc.edu/admissions/admission-requirements-master-of-science-in-biostatistics.

Core Courses

26-28 units.

PM 510L Principles of Biostatistics Units: 4
PM 511aL Data Analysis Units: 4
PM 511bL Data Analysis Units: 4
PM 512 Principles of Epidemiology Units: 4
PM 513 Experimental Designs Units: 3
PM 518a Statistical Methods for Epidemiological Studies I, II Units: 3

Statistical Theory Requirement

Students who wish to pursue a PhD in Biostatistics are recommended to enroll in PM 522a and PM 522b.

PM 522a Introduction to the Theory of Statistics Units: 3 and
PM 522b Introduction to the Theory of Statistics Units: 3
or
PM 618 Theory of Statistics for the Health Sciences Units: 4

Thesis

4 units.

PM 594a Master’s Thesis Units: 2
PM 594b Master’s Thesis Units: 2

Electives

Remaining units.

MATH 542 Analysis of Variance and Design Units: 3
MATH 543 Nonparametric Statistics Units: 3
MATH 545 Introduction to Time Series Units: 3
MATH 547 Mathematical Foundations of Statistical Learning Theory Units: 3
MATH 548 Sequential Analysis Units: 3
PM 511cL Data Analysis Units: 4
PM 516a Statistical Problem Solving Units: 1
PM 516b Statistical Problem Solving Units: 1
PM 518b Statistical Methods for Epidemiological Studies I, II Units: 3
PM 520L Advanced Statistical Computing Units: 3
PM 523 Design of Clinical Studies Units: 3
PM 534 Statistical Genetics Units: 3
PM 544L Multivariate Analysis Units: 3
PM 551 Statistical Methods in Genome-Wide Association Studies Units: 3
PM 552 Statistical Methods in Clinical Trials Units: 3
PM 560 Statistical Programming With R Units: 2
PM 566 Introduction to Health Data Science Units: 4
PM 569 Spatial Statistics Units: 3
PM 574 Programming In Modern Statistical Software Units: 2
PM 575 Statistical Methods in Environmental Epidemiology Units: 3
PM 579 Statistical Analysis of High-Dimensional Data Units: 4
PM 588 The Practice of Epidemiology Units: 4
PM 590 Directed Research Units: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
PM 591 Machine Learning for the Health Sciences Units: 4
PM 604 Health Behavior Research Methods Units: 4
PM 616 Neural Networks and Deep Learning Units: 3
PM 617 Introduction to Causal Inference in Epidemiology Units: 4

Additional Requirements

The student’s choice of elective courses will be directed by needs and interests and must be approved by the program director. When appropriate, elective courses not listed above may be substituted with the approval of the director. Sufficient familiarity in computer languages to operate major software packages for data management and analysis is required.