Program Details : University Catalogs : University of Minnesota

PUBH 6020 - Fundamentals of Social and Behavioral Science

Credits:	2.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Three major approaches to social sciences in public health: psychosocial, community approaches, economic and policy interventions. Covers theories of behavior change, program and policy development, community engagement, and policy implementation and advocacy. Not open to students in Community Health Promotion or Public Health Nutrition MPH programs.

PUBH 6102 - Issues in Environmental Health

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Current issues, principles, and methods of environmental/occupational health practice. prereq: Public health [MPH or MHA or certificate] student or health journalism MA major or nursing MS student or instr consent

PUBH 6250 - Foundations of Public Health

Credits:	2.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

In this course we will examine values, contexts, principles, and frameworks of public health. We will provide an introduction to public health, consider the history of public health, social/political determinants, impact of health disparities on race, class and gender, moral and legal foundations, public health structures, historical trauma and cultural competence, health and human rights, advocacy and health equity, communication and financing, and the future of public health in the 21st century. Grounded in theory and concepts, we will incorporate core competencies and skills for public health professionals and will focus on developing problem solving and decision-making skills through critical analysis, reflection, case studies, readings, and paper assignments.

PUBH 6741 - Ethics in Public Health: Professional Practice and Policy

Credits:	1.0 [max 1.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Introduction to ethical issues in public health practice/policy. Ethical analysis, recognizing/analyzing moral issues. prereq: Public health [MPH or MHA or certificate] student or environmental health [MS or PhD] major or instr consent

PUBH 6751 - Principles of Management in Health Services Organizations

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Understanding of and improvement in the competencies of managers in organizations, particularly as applied to health services and public health organizations. prereq: [Public hlth MPH or MHA or certificate] student or [environmental health MS or PhD] student or dentistry MS student or instr consent

PUBH 6320 - Fundamentals of Epidemiology

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.

PUBH 6341 - Epidemiologic Methods I

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 6320PubH /6341
Grading Basis:	A-F only
Typically offered:	Every Fall

Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.

PUBH 6450 - Biostatistics I

Credits:	4.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

This course will cover the fundamental concepts of exploratory data analysis and statistical inference for univariate and bivariate data, including: ? study design and sampling methods, ? descriptive and graphical summaries, ? random variables and their distributions, ? interval estimation, ? hypothesis testing, ? relevant nonparametric methods, ? simple regression/correlation, and ? introduction to multiple regression. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [College-level algebra, health sciences grad student] or instr consent

PUBH 6451 - Biostatistics II

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

This course will cover more advanced aspects of statistical analysis methods with a focus on statistical modeling, including: ? two-way ANOVA, ? multiple linear regression, ? logistic regression, ? Poisson regression, ? log binomial and ordinal regression, ? survival analysis methods, including Kaplan-Meier analysis and proportional hazards (Cox) regression, ? power and sample size, and ? survey sampling and analysis. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [PubH 6450 with grade of at least B, health sciences grad student] or instr consent

PUBH 7461 - Exploring and Visualizing Data in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.

PUBH 7462 - Advanced Programming and Data Analysis in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

This course is intended for students who are relatively proficient with R, and are looking to improve their coding and data analysis skills. The emphasis will be on learning tools and techniques which are useful to students who will be doing non-trivial programming and/or data analysis in either a research or production environment.

PUBH 7463 - Fundamentals of Prediction and Machine Learning for Public Health

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Most introductory statistics courses focus on concepts and techniques for statistical inference that allow researchers to quantify how strongly the available data support or contradict scientific hypotheses about the associations between covariates to outcomes of interest. Much less time is spent on the distinct problem of prediction, i.e., how to build a model using existing data that accurately predicts future outcomes based on individual characteristics. The prediction problem is of great interest in the health sciences; for example, the digitization of electronic health records and adoption of automated clinical decision support systems has made it possible for risk prediction algorithms to be tightly integrated into clinical care. At the same time, uncritical use of automated algorithms has the potential to increase health disparities. This course introduces key concepts and techniques that are relevant to using and assessing prediction models for biomedical data. Students will learn how to use statistical machine learning models to predict binary outcomes (logistic regression, classification trees, support vector machines) and continuous outcomes (linear regression, regression trees, generalized additive models), and how to compare the performance of multiple models using cross-validation and sample splitting. Additional topics will include ensemble methods, feature selection, clustering, common pitfalls in the use of prediction models for biomedical data, and issues of algorithmic fairness as they relate to health equity. For the final project, students will use a dataset in a biomedical area of interest to them to build and assess the performance of a prediction model. Methods will be illustrated and implemented in R.

PUBH 7465 - Biostatistics Consulting

Credits:	2.0 [max 3.0]
Typically offered:	Every Fall & Spring

This course examines the professional roles, responsibilities and analytic skills of the practicing biostatistician as consultant and collaborator in health science research. The spectrum of roles will be explored through lecture, readings, discussion, written assignments, and participation in statistical consulting sessions with investigators at the University of Minnesota and in the community. prereq: PubH 7405-7406 (or Stat 8051-8052) and Stat 5101-5102 (or Stat 8101-8102); Biostatistics graduate student.

PUBH 6342 - Epidemiologic Methods II

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Methods and techniques for designing, implementing, analyzing, and interpreting observational epidemiologic studies, including cohort, case-control, and cross-sectional studies.

PUBH 6809 - Advanced Methods in Health Decision Science

Credits:	3.0 [max 3.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Spring

Methods applicable to issues of medical decision making. Analyses of environmental/safety decisions. How to apply methods at cutting-edge of clinical decision science. prereq: [6717 or intro course in decision analysis], some facility with mathematical notation/reasoning

PUBH 7401 - Fundamentals of Biostatistical Inference

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth

PUBH 7430 - Statistical Methods for Correlated Data

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).

PUBH 7445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended

PUBH 7470 - Study Designs in Biomedical Research

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS

PUBH 7415 - Introduction to Clinical Trials

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 3415/PubH 7415
Typically offered:	Every Fall & Summer

Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent

PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent

CSCI 5707 - Principles of Database Systems

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4707/CSci 5707/INET 4707
Typically offered:	Every Fall

Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student

GEOG 5561 - Principles of Geographic Information Science

Credits:	4.0 [max 4.0]
Course Equivalencies:	Geog 3561/ Geog 5561
Typically offered:	Every Fall & Spring

Introduction to the study of geographic information systems (GIS) for geography and non-geography students. Topics include GIS application domains, data models and sources, analysis methods and output techniques. Lectures, reading, and hands-on experience with GIS software. prereq: grad

HINF 5430 - Foundations of Health Informatics I

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

An introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 5440 - Foundations of Translational Bioinformatics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics

HINF 5450 - Foundations of Precision Medicine Informatics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

The course will provide an introduction into the fundamental concepts of Precision Medicine with a focus on informatics-focused applications for clinical data representation, acquisition, decision making and outcomes evaluation. The student will gain an appreciation of fundamental biomedical data representation and its application to genomic, clinical, and population problems.

HINF 5502 - Python Programming Essentials for the Health Sciences

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5531 - Health Data Analytics and Data Science

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent

HINF 5610 - Foundations of Biomedical Natural Language Processing

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics

HINF 5630 - Clinical Data Mining

Credits:	3.0 [max 3.0]
Grading Basis:	OPT No Aud
Typically offered:	Periodic Fall

This is a hands-on introductory data mining course specifically focusing on health care applications. Analogously to the relationship between biostatistics and statistics, the data and computational challenges, the experiment design and the model performance requirements towards data mining in the clinical domain differ from those in general applications. This course aims to teach the students the most common data mining techniques and elaborate on the differences between general and clinical data mining. Specifically, the course will focus on (i) clinical data challenges and preprocessing; (ii) survey of the most common techniques in the clinical domain; (iii) clinical application touching up on experimental design and collaborations with physicians. The class will meet twice a week, one day dedicated to lectures and one day to a hands-on lab component, where students are expected to apply the techniques to health-related data. Some of the models will be evaluated with the involvement of a physician collaborator. Prerequisites: Basic linear algebra (matrix notation), basic optimization (gradient descent) Graduate level introductory statistics (e.g. STAT 5101-5102) or equivalent or instructor consent

MSBA 6331 - Big Data Analytics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Hive, Spark, scalable machine Learning, scalable real-time streaming analytics, NoSQL, cloud computing, and other recent developments in big data.

PUBH 6141 - GIS & Spatial Analysis for Public Health

Credits:	3.0 [max 3.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall

This course examines how to incorporate and handle spatial data to address public health questions, such as evaluating environmental exposures or identifying vulnerable and at-risk populations. We will utilize a Geographic Information System (GIS) to incorporate and visualize data for public health research. Classwork will be presented in the form of health-related case studies where GIS helps to formulate and address scientific hypotheses based on research topics in the School of Public Health. Specifically, the ArcGIS software will be used as a tool to integrate, manipulate, and display spatial health data. Topics include understanding spatial data, mapping, topology, spatial manipulations related to data structures, online data, geocoding, remote sensing imagery, and reviewing public health literature. The course will emphasize how to prepare spatial data for a formal statistical analysis. All coursework will be discussed in the context of statistical frameworks for evaluating geostatistical, point pattern, and area-level (or lattice) data examples. The intended audience for this course are masters and doctoral students who seek a more advanced understanding of GIS and spatial data beyond exploratory skills. Their goal should be a working knowledge of spatial analysis that can be readily applied in future research or employment. Students should leave this course prepared to take more advanced spatial analysis courses, map geographic trends, formulate scientific hypothesis for epidemiological applications, with the knowledge to acquire online spatial data, and the skills to critically evaluate published papers that utilize GIS.

PUBH 6325 - Data Processing with PC-SAS

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring

Introduction to methods for transferring/processing existing data sources. Emphasizes hands-on approach to pre-statistical data processing and analysis with PC-SAS statistical software with a Microsoft Windows operating system.

PUBH 6420 - Introduction to SAS Programming

Credits:	1.0 [max 1.0]
Typically offered:	Periodic Fall & Summer

Use of SAS for analysis of biomedical data. Data manipulation/description. Basic statistical analyses (t-tests, chi-square, simple regression).

PUBH 6739 - Data Dashboards and Visualization with Tableau

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring & Summer

The ability to analyze data is an essential skill for public health practitioners in all areas of professional practice. Data analysis is necessary to identify important emerging and/or current trends, problems, and issues that require action. It is important for anyone engaged in public health work to be able to locate relevant and accurate sources of data relative to public health issues, analyze it, synthesize it, and format it in a way that it is clear and compelling to specific audiences. This course provides an introduction to data analysis and presentation through the creation of a dashboard to present data. While this course uses Tableau software, the concepts covered in this course apply to any public health setting and are transferable to other dashboard and data visualization tools.

PUBH 7253 - Introduction to GIS

Credits:	1.0 [max 1.0]
Grading Basis:	S-N only
Typically offered:	Every Summer

Concepts/uses of Geographic Information Systems. Data structures, sources of data, tools, vendors/software, health-related applications. Exercises in spatial data display/query, map generation, spatial analysis using ArcGIS software. Students create their own GIS project model. prereq: Experience with spreadsheet programs

PUBH 7496 - Applied Practice Experience: Biostatistics

Credits:	1.0 [max 6.0]
Grading Basis:	S-N only
Typically offered:	Every Fall, Spring & Summer

MPH students are required to complete a supervised Applied Practice Experience (APEx). Students must address five competencies and must submit two products that demonstrate attainment of the competencies. prereq: biostatistics MPH student

PUBH 7494 - Integrative Learning Experience: Biostatistics

Credits:	1.0 -3.0 [max 3.0]
Grading Basis:	S-N only
Typically offered:	Every Fall, Spring & Summer

MPH students complete an integrative learning experience (ILE) that demonstrates synthesis of foundational and concentration competencies. Students in consultation with faculty select foundational and concentration-specific competencies appropriate to the student?s educational and professional goals. prereq: Biostatistics program, instr consent

University Catalogs

Public Health Data Science M.P.H.