Biostatistics Courses
BIOS 700 Introduction to Biostatistics. (3 – credit hours) (every fall, spring & summer). Health related statistical applications. Descriptive statistics, probability, confidence intervals, hypothesis testing, regression, correlation, ANOVA. May not be used for graduate credit in epidemiology or biostatistics.
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BIOS 701 Concepts and Methods of Biostatistics. (3 – credit hours) (every fall). Descriptive and inferential statistical applications to public health. Probability, interval estimation, hypothesis testing, measures of association. For students planning further study in epidemiology or biostatistics.
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BIOS 757 Intermediate Biostatistics. (3 – credit hours) ((every fall, spring & summer) (Prereq: A course in introductory statistics). Public health applications of correlation, regression, multiple regression, single and multi-factor analysis of variance and analysis of covariance.
BIOS 709 Basic Software for Public Health. (1 – credit hour) (every fall & summer). Statistical data management techniques. Microcomputer applications, communication between microcomputers and mainframe, tape and disk storage, access to large health-related databases.
BIOS 710 Effective Data Management for Public Health. (3 – credit hours) (every fall & summer). This course teaches techniques for creating and using small data sets. Students will become familiar with four software packages used for data entry, data management, and presentation, PC/SAS, STATA, MS Excel, and MS Access.
BIOS 711 Introduction to R Programming. (1 – credit hour) (every fall). Students will learn the software program R for performing data management. R software includes basic to advanced commands for properly formatting data for analysis for public health data.
BIOS 712 Introduction to Stata Software. (1 – credit hour) (every spring). Students will learn the software program Stata for performing data management. The course covers basic to advanced commands for properly formatting data for analysis for public health data.
BIOS 714 Introduction to MS Access for Public Health. (1 – credit hour) (every fall). This course focuses on the uses of Microsoft Access for data management in public health. The course takes the student through building tables, forms, queries, reports and finishes with automated scripts for each use with Access.
BIOS 719 Advanced SAS Methods for Public Health. (1 – credit hour) ((every fall, spring & summer). Building upon skills learned in BIOS 709 (Introduction to SAS), students will learn data management using PROC SQL & SAS Macro Language which prepares data for conducting efficient statistical analysis.
BIOS 735 Machine Learning for Public Health Applications. (3 – credit hours) (every even fall) (Prereq: BIOS 757 and BIOS 755). introduces the fundamental concepts and applications of machine learning (ML) in the context of public health, biostatistics, and related fields (e.g., epidemiology, psychology, neuroscience, genetics).
BIOS 740 Functional Analysis for Digital Health. (3 – credit hour) (every even fall). Introduces modern functional data analysis methods for modeling high-dimensional biomedical data that arise as functions, curves, or surfaces, with applications to digital health technologies (e.g., wearables and medical imaging) to analyze, quantify associations, and predict health outcomes.
BIOS 754 Discrete Data Analysis. (3 – credit hours) (every fall) (Prereq: BIOS 757 and EPID 700/701). Analysis of discrete data in public health studies. Relative risk, odds ratio, rates and proportions, contingency tables, logistic regression, introduction to other advanced topics. Not for Biostatistics majors.
BIOS 755 Introduction to Longitudinal Data Analysis. (3 – credit hours) (every spring) (Prereq: BIOS 757). An introduction to principles and methods for longitudinal data, which are often encountered in practice where multiple measures are observed over time on an individual. This course is designed for non- biostatistics major researchers, with a focus on data analysis and interpretation more than theoretical development. Problems will be motivated by applications in epidemiology and clinical medicine, health services research, and disease natural history studies.
BIOS 760 Biostatistical Methods in Clinical Trials. (3 – credit hours) (every even fall) (Prereq: EPID 700, BIOS 700, EPID 741, BIOS 757). This course will cover the basic and advanced statistical techniques necessary for the design, conduct, analysis and interpretation of results of clinical trials.
BIOS 761 Survival Analysis I. (3 – credit hours) (every fall) (Prereq: BIOS 757 or equivalent). Methods for the analysis of survival data in the biomedical setting. Underlying concepts; standard parametric and nonparametric methods for one or several samples; concomitant variables and the proportional hazards model.
BIOS 762 Genetic Statistics I. (3 – credit hours) (ever odd spring) This course Introduces key statistical methods and concepts for analyzing genomic data, including GWAS, differential expression and methylation, ChIP-seq, single-cell and spatial transcriptomics, and multi-omic integration, with applications to complex human diseases such as cancer and hands-on implementation using R and Bioconductor.
BIOS 767 Spatial Statistics for Public Health. (3 – credit hours) (every odd fall) (Prereq: BIOS 700/701 or equivalent). Spatial statistics methods commonly used in public health which includes mapping, tests for spatial correlation, hypothesis tests for spatial clustering, and regression models with spatial random effects.
BIOS 768 Signal and Network Analysis for Public Health (3 – credit hours) (every odd spring) Introduces computational methods for analyzing signals and networks in public health and biomedical research, including time-series and frequency-domain techniques, filtering, graph theory, network measures, random networks, and epidemic spreading, with emphasis on interpretation and hands-on application to real data.
BIOS 772 AI for Biomedical Data (3 – credit hours) (every even spring) Introduces deep learning architectures for analyzing large-scale biomedical data including genomics, electronic health records, and medical imaging, emphasizing model interpretability, ethical considerations, multi-modal data integration with real-world example applications, and implementation using Python.