The online Master of Science in Biostatistics (MSBST) is designed for professionals with a bachelor’s degree who aspire to start or advance their career in biostatistics, statistics or data science.
Offered by the School of Public Health & Information Science at the University of Louisville, this program delivers a forward-thinking curriculum that emphasizes medical, dental, nursing and healthcare applications of statistical analysis, with applicability across countless other industries. Through state-of-the-art coursework in designing research studies and applying modern statistical analysis, you’ll be equipped with the expertise needed to meet the growing industry demand for skilled statisticians who can effectively drive evidence-based decisions.
Complete this degree on your own time through fully online coursework and learning tools.Learn More
Only 12 courses are required for graduation. Math Tools prep course also available in summer.Learn More
To-date, all graduates of the MSBST program at UofL have found employment in a biostatistics-related role
$764 per credit hour
$250 active duty military rate per credit hour
Tuition rate does not include costs associated with a specific course or program, such as textbooks. Tuition and fees are subject to change at any time without prior notice.
Ongoing innovation in a variety of industries creates the need for skilled statisticians and bioinformatics professionals to drive evidence-based decisions in medicine, nursing, public health, commerce and engineering. The field is projected to experience a staggering 34% growth rate from 2014-2024. In 2016, statisticians earned a median annual salary of $80,500 (BLS.gov).
Graduates of our online MSBST program are equipped with the expertise needed to pursue careers with:
Get a feel for the industry with an online Graduate Certificate in Biostatistics—a 12 credit hour (4 course) program that can be the perfect platform to launch your next education achievement.
|Application Deadline||Term||Start Date|
Note: Electronic transcripts are only accepted directly from the institution(s). Please have electronic transcripts sent directly to UofL Graduate Admissions Office.
Send all materials to:
University of Louisville
2211 S. Brook Street
Louisville, KY 40292
The Master’s in Biostatistics program is open to international students. If you live outside of the United States and intend to complete an online academic program from your home country, be sure to review these additional requirements for international students.
For more information on the admission and application process, please contact our Online Learning Enrollment Counselor at 800.871.8635 or by email at email@example.com.
The online M.S. in Biostats degree is a 32 credit hour program that requires 24 credit hours in core courses and 8 credit hours in electives.
|PHST 661 Probability||3|
|PHST 680 Biostatistical Methods I||3|
|PHST 624 Clinical Trials I||2|
|PHPH 523 Public Health in the U.S.1||(2)|
|PHST 662 Mathematical Statistics||3|
|PHST 681 Biostatistical Methods II||3|
|PHST 684 Categorical Data Analysis||3|
|PHST 625 Clinical Trials II||2|
|PHST 683 Survival Analysis||3|
Students may also choose elective courses outside of the department in fields related to biostatistics, such as Mathematics, Epidemiology, and Computer Science (subject to approval from faculty advisor). Students are responsible for ensuring they have met the prerequisites for these courses.
PHST 661 Probability
Prerequisite: Enrolled in BDSCMS BIO or Math major in Graduate School. Introduction to probability theory. Topics include axioms of probability, conditional probability, discrete and continuous random variables, probability distributions and joint distributions, moments, moment generating functions, mathematical expectation and transformation of random variables, limit theorems (Law of Large Numbers and Central Limit Theory).
PHST 680 Biostatistical Methods I
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 501, Math major in Graduate School. A mathematically sophisticated presentation of statistical principles and methods. Topics include exploratory data analysis, graphical methods, point and interval estimation, hypothesis testing, and categorical data analysis Matrix algebra is required. Data sets drawn from biomedical and public health literature will be analyzed using statistical computer packages.
PHST 624 Clinical Trials I
Prerequisite: Enrolled in BDSCMS BIO, CISCCCI, CISCMSC, MD/MSc. Phases of Trials, Ethical Issues, Basic Design, Inclusion and Exclusion criteria, Randomization and Blinding, Sample Size, Monitoring Response Variables, and Issues in Data Analysis
PHST 662 Mathematical Statistics
Prerequisite: Enrolled in BDSCMS BIO, Math major in Graduate School and PHST 661. A first course in statistical theory. Topics include limiting distributions, maximum likelihood estimation, least squares, sufficiency and completeness, confidence intervals, Bayesian estimation, Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests and asymptotic distributions.
PHST 681 Biostatistical Methods II
Prerequisite: Enrolled in BDSCMS BIO, PH MPH, Math major in Graduate School and PHST 680. This course offers a mathematically sophisticated introduction to simple regression models and analysis of variance. Matrix algebra is required and data analysis will be illustrated drawing examples from biomedical and public health literature.
PHST 684 Categorical Data Analysis
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 501. Topics include inference for two-way contingency tables, models for binary response variables, log-linear models, models for ordinal data, multinomial response data, Poisson regression and analysis of repeated categorical response data. Emphasis will be placed on methods and models most useful in biomedical and public health research.
PHST 625 Clinical Trials II
Prerequisite: PHST 624 and enrolled in BDSCMS BIO, CISCCCI, CISMSC, MD/MSc. Sample Size and Power Analysis, Survival Analysis, Sequential Design, Meta Analysis, Reporting and Interpreting of Results, Multicenter Trails. SPSS will be used.
PHST 683 Survival Analysis
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 681. Statistical methods for analyzing survival data. Parametric and nonparametric methods for complete and incomplete data, life-table, KM estimator, accelerated lifetime models, proportional hazard models, log-rank tests, and goodness-of-fit tests.
PHST 620 Introduction to Statistical Computing
Prerequisite: Enrolled in PH MPH and PHST 500. This course provides an introduction to SAS. It will give students an overview of the SAS system under MS Windows and provide fundamental grounding in the environment for accessing, structuring, formatting and manipulating data. Students will learn how to summarize and display data, and the inference between data steps and procedures to get information out of data.
PHST 682 Multivariate Statistics
Prerequisite: Enrolled in BDSCMS BIO, Math major in Graduate School and PHST 681. The topics covered in this course include the multivariate normal distribution, inference for mean vectors, inference for covariance and correlation matrices, analysis of covariance structure, analysis of serial measurements, factor analysis, and discriminant analysis. Statistical methods and models that are most useful in multivariate data analysis will be introduced. Instruction will also be given in the proper use of R to carry out these analyses.
Jeremy received in his Ph.D. in Statistics from the University of Florida in 2013. His areas of research include Bayesian statistics, longitudinal data, variable selection, and missing data models. He is the instructor for the PHST 661 Probability and PHST 662 Mathematical Statistics sequence of courses.
Bakee earned his Ph.D. from Case Western University in 2013. His primary research interests include stable and Linnik processes, logistic regression and clinical trials. He teaches PHST 620 - Introduction to Statistical Computing (SAS), PHST 624 - Clinical Trials I, PHST 625 - Clinical Trials II and PHST 640 - Statistical Methods for Research Design in Health Studies.
Riten earned his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2010. His methodological research centers on Bayesian models and their applications in genomics and proteomics. He is an instructor for PHST 682 - multivariate statistical analysis.
Rebekah received her Ph.D. in Applied and Industrial Mathematics from the University of Louisville in 2012 with her research primarily focused on segmented regression models. She joined the Biostatistics Department in the fall of 2016 and is the instructor for PHST 680 & 681 (Biostatistical Methods I & II) for the online Master of Science in Biostatistics.
Qi earned his Ph.D. from, Clemson University in 2013. His areas of research include high dimensional data, nonparametric modeling and survival data analysis. He teaches PHST 684 - Categorical Data Analysis.
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