Program Details : University Catalogs : University of Minnesota

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

CSCI 1933 - Introduction to Algorithms and Data Structures

Credits:	4.0 [max 4.0]
Course Equivalencies:	CSci 1902/CSci 1933/CSci 1933H
Typically offered:	Every Fall, Spring & Summer

Advanced object oriented programming to implement abstract data types (stacks, queues, linked lists, hash tables, binary trees) using Java language. Inheritance. Searching/sorting algorithms. Basic algorithmic analysis. Use of software development tools. Weekly lab. prereq: 1133 or instr consent

MATH 1271 - Calculus I (MATH)

Credits:	4.0 [max 4.0]
Course Equivalencies:	Math 1271/Math 1281/Math 1371/
Typically offered:	Every Fall, Spring & Summer

Differential calculus of functions of a single variable, including polynomial, rational, exponential, and trig functions. Applications, including optimization and related rates problems. Single variable integral calculus, using anti-derivatives and simple substitution. Applications may include area, volume, work problems. prereq: 4 yrs high school math including trig or satisfactory score on placement test or grade of at least C- in [1151 or 1155]

CSCI 2033 - Elementary Computational Linear Algebra

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

Matrices/linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations, Eigenvalues, singular values. Algorithms/computational matrix methods using MATLAB. Use of matrix methods to solve variety of computer science problems. prereq: [MATH 1271 or MATH 1371], [1113 or 1133 or knowledge of programming concepts]

MATH 4242 - Applied Linear Algebra

Credits:	4.0 [max 4.0]
Course Equivalencies:	Math 4242/Math 4457
Typically offered:	Every Fall, Spring & Summer

Systems of linear equations, vector spaces, subspaces, bases, linear transformations, matrices, determinants, eigenvalues, canonical forms, quadratic forms, applications. prereq: 2243 or 2373 or 2573

STAT 3011 - Introduction to Statistical Analysis (MATH)

Credits:	4.0 [max 4.0]
Course Equivalencies:	AnSc 3011/ESPM 3012/Stat 3011/
Typically offered:	Every Fall, Spring & Summer

Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.

STAT 3021 - Introduction to Probability and Statistics

Credits:	3.0 [max 3.0]
Course Equivalencies:	STAT 3021/STAT 3021H
Typically offered:	Every Fall, Spring & Summer

This is an introductory course in statistics whose primary objectives are to teach students the theory of elementary probability theory and an introduction to the elements of statistical inference, including testing, estimation, and confidence statements. prereq: Math 1272

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 8430 - Foundations of Health Informatics I Lab

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

The PhD-level lab complement for 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.

HINF 5436 - AHC Informatics Grand Rounds

Credits:	1.0 [max 10.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Presentation/discussion of research problems, current literature/topics of interest in Health Informatics.

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 8440 - Foundations of Translational Bioinformatics Lab

Credits:	2.0 [max 2.0]
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 8525 - Health Informatics Teaching

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Spring Even Year

Use selected teaching techniques to assist in the delivery of course content in health informatics curriculum. Work with a professor who is the course director. From evaluation and feedback on their teaching technique, students develop a teaching philosophy as a final course project. prereq: HINF student or instr consent

HINF 8888 - Thesis Credit: Doctoral

Credits:	1.0 -24.0 [max 100.0]
Grading Basis:	No Grade
Typically offered:	Every Fall, Spring & Summer

(No description) prereq: PhD candidate or department consent. Max 18 credits per semester; 24 credits required

HINF 5431 - Foundations of Health Informatics II

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

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 8431 - Foundations of Health Informatics II Lab

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

The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.

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 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety

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

Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. 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

NURS 5116 - Consumer Health Informatics

Credits:	2.0 [max 2.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall

This course examines issues from the consumer?s perspective in the acquisition, understanding, or use of health information. Mobile health, telehealth, sensor technology, and internet sources for improving health are examined. The impact on consumer-provider communication and relationships as well as ethical and legal issues are explored. prereq: Grad student or instr consent

NURS 7108 - Population Health Informatics

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

Standards, interoperability, and integration of information systems for population health are examined. Population health use cases are analyzed for potential benefits, legal, ethical, and practical issues related to the development of population health information systems. prereq: [5115 or [HINF 5430, HINF 5431]] or instr consent

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

BIOC 5361 - Microbial Genomics and Bioinformatics

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

Introduction to genomics. Emphasizes microbial genomics. Sequencing methods, sequence analysis, genomics databases, genome mapping, prokaryotic horizontal gene transfer, genomics in biotechnology, intellectual property issues. Hands-on introduction to UNIX shell scripting, genomic data analysis using R and Excel in a computer lab setting. prereq: College-level courses in [organic chemistry, biochemistry, microbiology]

BIOC 8007 - Molecular Biology of the Genome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8008 - Molecular Biology of the Transcriptome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

CGSC 8410 - Perspectives in Learning, Perception, and Cognition

Credits:	2.0 [max 24.0]
Grading Basis:	S-N only
Typically offered:	Every Fall & Spring

Lectures/discussions in cognitive sciences by local/visiting faculty.

CSCI 5106 - Programming Languages

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

Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages. prereq: 4011 or instr consent

CSCI 5115 - User Interface Design, Implementation and Evaluation

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

Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project. prereq: 4041 or instr consent

CSCI 5271 - Introduction to Computer Security

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

Concepts of computer, network, and information security. Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls. prereq: 4061 or 5103 or equiv or instr consent

CSCI 5421 - Advanced Algorithms and Data Structures

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

Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent

CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics

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

Computational methods for analyzing, integrating, and deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets. prereq: 3003 or 4041 or instr consent

CSCI 5481 - Computational Techniques for Genomics

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

Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks. prereq: 4041 or instr consent

CSCI 5511 - Artificial Intelligence I

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4511W/CSci 5511
Prerequisites:	[2041 or #], grad student
Typically offered:	Every Fall

Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student

CSCI 5521 - Machine Learning Fundamentals

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

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives

CSCI 5525 - Machine Learning: Analysis and Methods

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent

CSCI 5607 - Fundamentals of Computer Graphics 1

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

Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research. prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081

CSCI 5608 - Fundamentals of Computer Graphics II

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

Advanced topics in image synthesis, modeling, rendering. Image processing, image warping, global illumination, non-photorealistic rendering, texture synthesis. Parametric cubic surfaces, subdivision surfaces, acceleration techniques, advanced texture mapping. Programming in C/C++. prereq: 5607 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

CSCI 5708 - Architecture and Implementation of Database Management Systems

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

Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules. prereq: 4041 or 4707 or 5707 or instr. consent

CSCI 5801 - Software Engineering I

Credits:	3.0 [max 3.0]
Prerequisites:	2041 or #
Typically offered:	Every Fall

Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent

CSCI 8725 - Databases for Bioinformatics

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

DBMS support for biological databases, data models. Searching integrated public domain databases. Queries/analyses, DBMS extensions, emerging applications. prereq: 4707 or 5707 or instr consent

DES 5185 - Human Factors in Design

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

Exploration of the theories and methods that influence the assessment of physical, cognitive, social, and psychological human factors, and the analysis of user needs with application to designed products and systems that interact with a human user or the human body. This course is an introductory overview to the theories and concepts of Human Factors and their application through the methods of User-Centered Design. Typically, the class is comprised of students from a wide variety of disciplines and backgrounds. Course material is explored through readings, lectures, discussions, case studies, and course projects.

EPSY 5244 - Survey Design, Sampling, and Implementation

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

Survey methods, including mail, phone, and Web-based/e-mail surveys. Principles of measurement, constructing questions/forms, pilot testing, sampling, data analysis, reporting. Students develop a survey proposal and a draft survey, pilot the survey, and develop sampling/data analysis plans. prereq: [5221 or 5231 or 5261 or equiv], [CEHD grad student or MEd student]

EPSY 5262 - Intermediate Statistical Methods

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

Application of statistical concepts/procedures. Analysis of variance, covariance, multiple regression. Experimental design: completely randomized, block, split plot/repeated measures. prereq: 3264 or 5261 or equiv

GCD 8103 - Human Histology

Credits:	5.0 [max 5.0]
Course Equivalencies:	GCD 6103/8103
Typically offered:	Every Fall

Light/electron microscopic anatomy of tissues and their organization into human organs. Emphasizes integrating structure, its relationship to function at levels from molecules to organs. Lecture, lab. prereq: Undergraduate biology, chemistry, math, and physics course; instr consent

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 5494 - Topics in Health Informatics

Credits:	1.0 -3.0 [max 9.0]
Typically offered:	Periodic Fall & Spring

Topics in health informatics. prereq: Professional student or grad student or instr consent

HINF 5496 - Internship in Health Informatics

Credits:	1.0 -6.0 [max 18.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent

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 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 5620 - Data Visualization for the Health Sciences

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

An advanced health informatics course, focusing on theoretical and practical aspects of data and information visualization for health care and the health sciences. Topics include classic and novel visualization types; models of human visual perception and cognition; color, text and typography; maps and diagrams; evaluation and testing; and the aesthetic and cultural aspects of visualization. Examples emphasize health sciences applications for clinicians, patients, researchers, and analysts. Modern programming and commercial tools are discussed, including D3, ggplot2, and Tableau. Students will report on and discuss visualization methods, published studies and books, culminating in a final visualization project of the student's choosing.

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

HINF 5640 - Advanced Translational Bioinformatics Methods

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

This course is designed to introduce the high throughput platforms to students who are interested in the genomics research and genomics data analysis in the basic and clinical medical science field. The course covers history of the genomics platforms, its revolution and the specifics of the data generated by all existing different platforms. The course will also introduce all existing sequencing platforms and applications to biological science, as well the current trends in this field.

HINF 5650 - Integrative Genomics and Computational Methods

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

Genome-scale high throughput data sets are a central feature of modern biological research and translational clinical study. Experimental, computational biologists and clinical researchers who want to get the most from their data sets need to have a firm grasp and understanding of genomic data structure characteristics, analytical methodology and the intrinsic connection to integrate. This course is designed to build competence in quantitative methods for the analysis of high-throughput genomic data and data integration.

HINF 8220 - Computational Causal Analytics

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

Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.

HINF 8405 - Advanced Topics in Health Informatics I

Credits:	1.0 -4.0 [max 12.0]
Typically offered:	Every Fall

Topics may include computer systems design for health sciences, small computer concepts/use, computers for clinical services, computer-aided medical decision making, biomedical image processing, pattern recognition, data mining. Case studies from health sciences. prereq: Professional student or grad student or instr consent

HINF 8406 - Advanced Topics in Health Informatics II

Credits:	1.0 -4.0 [max 12.0]
Typically offered:	Every Spring

This is a topics course. Topics may include, computational causal discovery for health sciences, computer systems design for health sciences, small computer concepts and use, computers for clinical services, computer-aided medical decision making, biomedical image processing, and pattern recognition. Case studies from health sciences.

HINF 8492 - Advanced Readings or Research in Health Informatics

Credits:	1.0 -6.0 [max 24.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall, Spring & Summer

Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent

HINF 8535 - Advanced Health Informatics Research Methods

Credits:	3.0 [max 3.0]
Prerequisites:	HINF student or #
Grading Basis:	A-F only
Typically offered:	Spring Even Year

Application of research methods, evaluation. Design, data collection, and data analysis in the context of health informatics, including computational and health data challenges. prereq: HINF student or instr consent

IDSC 6041 - Information Technology Management

Credits:	2.0 [max 2.0]
Course Equivalencies:	IDSc 6040/MBA 6241
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Management of information systems, information technology (IT) in global organization. Strategic uses of IT. Alignment of IT, organizational strategy, internet/Web technologies, e-commerce customer services. Integration of e-business applications, interorganizational systems, systems implementation. Management of information as resource. Lecture, case analysis, classroom discussion. Prereq MBA student.

IDSC 6051 - Information Technologies and Solutions

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

Current/emerging technologies in modern Net-enhanced organizations. Internet/Web technologies, including Internet fundamentals, Web communications, Web 2.0/social media, information security, cloud computing, IT-driven innovation, emerging IT trends.

IDSC 6471 - Knowledge Management

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

Design, evaluation, use of knowledge in organizations. Leveraging knowledge in workers, structures, processes. Assessment of knowledge needs. Evaluation of key decision processes, information demands, usage patterns, content requirements. Behavioral/cultural barriers. Use of technology for knowledge management. prereq: MBA student

IDSC 8721 - Behavioral Decision Theory

Credits:	3.0 [max 3.0]
Prerequisites:	Business admin PhD student or #; offered alt yrs
Typically offered:	Periodic Fall & Spring

Traditional/current research. Major models/methodologies. Issues of preference, judgment, and choice under conditions of certainty/uncertainty. Seminar format. prereq: Business admin PhD student or instr consent; offered alt yrs

IE 8521 - Optimization

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

Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.

IE 8531 - Discrete Optimization

Credits:	4.0 [max 8.0]
Typically offered:	Periodic Fall & Spring

Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.

KIN 5001 - Foundations of Human Factors/Ergonomics

Credits:	3.0 [max 3.0]
Course Equivalencies:	HumF/Kin 5001
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Variability in human performance as influenced by interaction with designs of machines and tools, computers and software, complex technological systems, jobs and working conditions, organizations, and sociotechnical institutions. Emphasizes conceptual, empirical, practical aspects of human factors/ergonomic science.

LING 5001 - Introduction to Linguistics

Credits:	4.0 [max 4.0]
Course Equivalencies:	Ling 3001/3001H/5001
Typically offered:	Every Fall, Spring & Summer

Scientific study of human language. Methods, questions, findings, and perspectives of modern linguistics. Components of the language system (phonetics/phonology, syntax, semantics/pragmatics); language acquisition; language and social variables; language and cognition; language change; language processing; language and public policy; language and cognition.

LING 5205 - Semantics

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

Analysis of sentence meaning. Semantic properties. Relations such as analyticity, entailment, quantification, and genericity. Philosophical background, formal techniques of semantic analysis, how sentence meaning depends on word meaning, syntax, and context. The role of semantics in grammatical theory. prereq: [4201 or 5201] or instr consent

LING 5801 - Introduction to Computational Linguistics

Credits:	3.0 [max 3.0]
Typically offered:	Spring Odd Year

Methods/issues in computer understanding of natural language. Programming languages, their linguistic applications. Lab projects. prereq: [4201 or 5201] or programming experience or instr consent

MATH 5445 - Mathematical Analysis of Biological Networks

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

Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations

MATH 5467 - Introduction to the Mathematics of Image and Data Analysis

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

Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended

MATH 5652 - Introduction to Stochastic Processes

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

Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101

MEDC 5245 - Introduction to Drug Design

Credits:	3.0 [max 3.0]
Course Equivalencies:	Chem 5245/Phar 6245/MedC 5245
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Concepts that govern design/discovery of drugs. Physical, bioorganic, medicinal chemical principles applied to explain rational design, mechanism of action drugs. prereq: Chem

MILI 6992 - Healthcare Delivery Innovations:Optimizing Cost and Quality

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

Understand stakeholders that impact healthcare delivery including providers, payers, employers and patients and how they are trying to transform this unique value chain to improve care while reducing cost.

MILI 6995 - Medical Industry Valuation Laboratory

Credits:	2.0 [max 6.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Interdisciplinary student teams create rapid production market analysis of promising medical technologies/services to determine potential for success in market. Exposure to University innovations, venture firms, inventors. prereq: Grad student

NURS 5115 - Nursing Informatics and Digital Health Technologies

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

This course will provide an introduction to the field of nursing informatics and its role in supporting care processes to ensure safe, high-quality, and efficient healthcare. Nurses, as members of multidisciplinary healthcare teams, play an important role in the implementation and governance of these informatics and digital health tools. Skills on using data from these tools to generate knowledge, drive decision-making, and support equitable access to quality, safe care will be shared. Nurse contributions in using digital technologies, influencing policies, and emerging trends in the field will be discussed.

NURS 5117 - Consumer Health Informatics Practicum

Credits:	2.0 [max 2.0]
Grading Basis:	S-N only
Typically offered:	Every Fall

Students apply consumer health informatics principles, theories, and research to consumer health informatics topics and how technology is used to engage patients, clinicians, and family members in their health care. Specific topics include electronic health literacy, digital/mobile health technologies (health apps), and sensor/remote monitoring. prereq: Graduate student, [Nurs 5116 or &Nurs 5116] or instructor consent

NURS 6105 - Systems Analysis and Design

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

Role of informatian in interprofessional team for analysis and design of information systems. Concepts/theories of systems analysis, system life cycle, project management, system requirements, human factors. Evaluation of use of health information systems. prereq: 5115 or equiv or instr consent

NURS 7106 - Knowledge Representation and Interoperability Practicum

Credits:	2.0 [max 2.0]
Grading Basis:	S-N only
Typically offered:	Every Summer

Knowledge representation and interoperability principles/standards to improving knowledge in clinical or public health settings. Applied knowledge representation to nursing. prereq: [NURS 5115 or instr consent], [NURS 7105 or concurrent registration is required (or allowed) in NURS 7105]

NURS 7109 - Population Health Informatics Practicum

Credits:	2.0 [max 2.0]
Grading Basis:	S-N only
Typically offered:	Every Fall

Apply principles, theories, and standards to integration of data to solve a particular population health problem. prereq: [5115, [[7108 or concurrent registration is required (or allowed) in 7108] or [HINF 5430, HINF 5431]]] or instr consent

NURS 7113 - Clinical Decision Support: Theory

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

Principles and concepts of knowledge management and decision making for support of clinical practice. Students design a clinical decision support intervention and examine the legal, ethical, and practical issues related to its implementation and maintenance of CDS interventions. prereq: 5115 or HINF 5430/5431 or instr consent

NURS 7114 - Clinical Decision Support Practicum

Credits:	2.0 [max 2.0]
Grading Basis:	S-N only
Typically offered:	Every Spring

Apply clinical decision support knowledge to the development and application of a clinical decision support intervention. prereq: [5115, [[7113 or concurrent registration is required (or allowed) in 7113] or [HINF 5430, HINF 5431]]] or instr consent

NURS 7118 - Human Factors and Human-Computer Interaction in Health Informatics

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

Principles of human factors and human-computer interaction to optimize research/practice in nursing and health informatics. Interactive system design that accommodates/enhances capabilities of user. prereq: Biostatistics or instr consent

NURS 7610 - Nurses Leading Change and Innovation to Transform Healthcare

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

This course will prepare doctoral nursing students for advanced nursing practice to integrate whole systems thinking, contemporary theories, and factors contributing to effective leadership in multi-sectoral settings. The focus includes: team dynamics, partnership in practice, innovation, inclusivity, health equity, and wellbeing. The vision is to prepare nursing leaders to transform healthcare for improvement of health for all people.

PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine

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

This course is for individuals wanting advanced knowledge in PGx and an introduction to the broader field of precision medicine. It consists of lectures, homework assignments and class discussions designed to introduce precision medicine as it relates to the impact of genetic variation on health and the advanced practice of pharmacogenomics. Students will learn diagnostic methods to identify germline and somatic mutations, how algorithms and equations are used for predictions and the relationships between genes and environment. How these concepts apply in the clinical settings, forecast health and drug response will be presented. Emerging tools in genetics and pharmacogenomics will be presented. The course will also address clinical implementation and the ethical, legal, and social issues presented by precision medicine and PGx. Prerequisite: 3rd year PharmD student, graduate student in healthcare or related program, or equivalent experience or instructor consent

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 6131 - Working in Global Health

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

Introduction to key issues in global health. Global burden of disease. Cultural issues/health. Nutrition. Infectious diseases. Environmental problems. Women/children. Prereq Grad student.

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 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 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 6386 - Cardiovascular Disease Epidemiology and Prevention

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

The course will provide an introduction to cardiovascular disease (CVD) epidemiology. It is intended to provide a detailed perspective on the well-established risk factors for CVD, as well as an introduction to emerging risk factors. Both observational studies and clinical trials will be discussed. The class will include a main focus on prevention of cardiovascular disease, and national recommendations for treatment and prevention. Several classes will incorporate discussions of new directions and current controversies in CVD. Additionally, the class will introduce students to the CVD research in the Division of Epidemiology and Community Health.

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 6541 - Statistics for Health Management Decision Making

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

Variation. Frequency distribution, measurement, probability, graphing. Significance tests, estimation, trends; data handling. Modeling, odds ratios. Prevalence, incidence and vital statistics. Research applications. Statistical approach to rational administrative decision making. Inductive teaching, lectures, computer/lab exercises. prereq: Health care admin student or instr consent

PUBH 6555 - Health Economics

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

General principles of health economics applied to issues in health. Implications for health policy.

PUBH 6556 - Health and Health Systems

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

U.S. health care system and health policy process, including current challenges in the areas of health care delivery, financing, and policy.

PUBH 6557 - Health Finance I

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

Principles of corporate/not-for-profit finance. Net present value, financial analysis, capital budgeting, financing options/decisions, capital structure, capital asset pricing model, financial planning, working capital management. prereq: [[Health care admin or public health admin/policy major], familiarity with computerized spreadsheets] or instr consent

PUBH 6558 - Health Finance II

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

Principles of corporate/not-for-profit finance and insurance concepts integrated/applied to health care. Capital/operating budgets. Medicare's payment systems for hospitals/physicians, risk-adjusted capitation payment systems. Population-based health care finance, managed care. Financing aspects of public health policy and health care reform. prereq: MHA student, familiarity with computerized spread-sheets]] or permission of instructor

PUBH 6560 - Operations Research and Quality in Health Care

Credits:	3.0 [max 3.0]
Prerequisites:	Grad-level statistics/management coursework
Grading Basis:	A-F only
Typically offered:	Every Fall

Using a systems perspective to develop models to analyze/improve health care operations. Identifying data needs/sources to model structures, processes, and outcomes of care. Applying quality improvement, management sciences/operations research techniques to real world health care problems. prereq: Grad-level statistics/management coursework

PUBH 6562 - Information Technology in Health Care

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

Managing information as a strategic resource within health care organizations. Designing information technology systems to capture, combine, and transform information to measure processes/outcomes of care, support collaborative clinical decision making, support management decisions, empower patients, and improve health care operations. Prereq: MHA student or instructor consent.

PUBH 6564 - Private Purchasers of Health Care: Roles of Employers and Health Plans in U.S. Health Care System

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Development and organization of HMOs and PPOs: risk sharing, provider contracts, utilization management, quality improvement, marketing, and new product development; employer relations; Medicare and Medicaid contracting; budget processing; financial performance; pricing; government regulations. prereq: MHA or MBA or HSRP or PHA student or instr consent

PUBH 6565 - Innovation of Healthcare Services

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

Designing/creating new care delivery services/experiences. Exploiting opportunities for innovation. Overcoming obstacles. Capturing value. prereq: MHA student only

PUBH 6717 - Decision Analysis for Health Care

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

Introduction to methods/range of applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation.

PUBH 6724 - The Health Care System and Public Health

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

Overview of health care delivery, finance systems within public health context. Components of health care system: financing, role of employers/public programs, health care delivery system, managed care. Collaborative interventions between managed care, public health. prereq: Public health or grad student or instr consent

PUBH 6742 - Ethics in Public Health: Research 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 research/policy. Ethical analysis. Recognizing/analyzing moral issues.

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 6765 - Continuous Quality Improvement: Methods and Techniques

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

Theory/practical applications of concepts, tools, techniques of continuous quality improvement (QI) in public health/health care.

PUBH 6780 - Topics in Public Health Administration and Policy

Credits:	1.0 -3.0 [max 60.0]
Typically offered:	Periodic Fall, Spring & Summer

New courses or topics of interest in public health administration/policy.

PUBH 6800 - Topics: Health Services Research and Policy

Credits:	0.5 -4.0 [max 80.0]
Typically offered:	Periodic Fall, Spring & Summer

New courses or topics of interest in health services research and policy.

PUBH 6803 - Conducting a Systematic Literature Review

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

Project-based class to develop systematic review skills for evidence-based practice. Draws from AHRQ and Cochrane systematic review methodology; supported by examples from the Minnesota Evidence-based Practice Center. Use for master?s thesis, dissertation, or to support research proposals. Prereq: research study design or epidemiology.

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 6832 - Economics of the Health Care System

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

Examines applications of microeconomic principles to the U.S. health care system. Topics include demand for medical care, insurance theory and selection issues, provider payment, competition in health care markets, the health care workforce, pharmaceutical prices and innovation, health care spending growth, quality of care, externalities, the relationship between income and health, and the economics of the opioid epidemic. Prerequisite: an introductory economics or microeconomic theory course ? or permission of the instructor.

PUBH 6862 - Cost-Effectiveness Analysis in Health Care

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

Government regulations. New technologies. Diagnosis/treatment protocols. Strengths, limitations, appropriateness of different approaches. prereq: instr consent; introductory econ course recommended

PUBH 6863 - Understanding Health Care Quality

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

Introduction to assessing/assuring quality of care. Emphasizes both process and outcomes approaches, paralleling interest in appropriateness/effectiveness of care. Issues around creating needed behavioral changes.

PUBH 7400 - Topics: Biostatistics

Credits:	0.5 -4.0 [max 20.0]
Typically offered:	Periodic Fall, Spring & Summer

New courses or topics of interest in biostatistics.

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 7402 - Biostatistics Modeling and Methods

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

Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences

PUBH 7405 - Biostatistical Inference I

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

T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent

PUBH 7407 - Analysis of Categorical Data

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

Contingency tables, odds ratio, relative risk, chi-square tests, log-linear models, logistic regression, conditional logistic regression, Poisson regression, matching, generalized linear models for independent data. SAS/S-Plus used throughout. prereq: 7405, [Stat 5102 or concurrent registration is required (or allowed) in Stat 5102 or Stat 8102 or concurrent registration is required (or allowed) in Stat 8102]

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

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 7440 - Introduction to Bayesian Analysis

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

Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent

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 7460 - Advanced Statistical Computing

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

Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent

PUBH 7475 - Statistical Learning and Data Mining

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

Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended

PUBH 7588 - Information Uses in Long-Term Care

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Accumulation/analysis of data to inform management decision-making in long-term care. One day on-campus seminar, independent study. prereq: Some knowledge of computers

PUBH 8432 - Probability Models for Biostatistics

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

Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent

PUBH 8442 - Bayesian Decision Theory and Data Analysis

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

Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent

PUBH 8445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 7445/PubH 8445
Typically offered:	Fall Odd Year

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: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable

PUBH 8446 - Advanced Statistical Genetics and Genomics

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

Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent

PUBH 8452 - Advanced Longitudinal Data Analysis

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

Methods of inference for outcome variables measured repeatedly in time or space. Linear/nonlinear models with either normal or non-normal error structures. Random effects. Transitional/marginal models with biomedical applications. prereq: [Stat 5102, Stat 8311, experience with [SAS or S+], advanced [biostats or stat] student] or instr consent

PUBH 8462 - Advanced Survival Analysis

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

Statistical methods for counting processes. Martingale theory (transforms, predictable processes, Doob decomposition, convergence, submartingales). Applications to nonparametric intensity estimation. Additive/relative risk models. Inference for event history data, recurrent events, multivariate survival, diagnostics. prereq: [7450, 8432, Stat 5102, advanced [biostatistics or statistics] major] or instr consent

PUBH 8472 - Spatial Biostatistics

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

Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended

PUBH 8801 - Health Services Policy Analysis: Theory

Credits:	1.0 [max 3.0]
Typically offered:	Every Fall

Course introduces students to the research and theoretical aspects of health policy, to enhance understanding of of the equity, historical, and socio-cultural, and political context of health policy, to develop deep fluency in the health policy process and policy-relevant aspects of health services research. prereq: PubH: HSRPA major or instr consent

PUBH 8810 - Research Studies in Health Care

Credits:	3.0 [max 6.0]
Typically offered:	Every Fall

Introduction to philosophy of science, conceptual modeling, experimental design, survey/sample design, issues relevant to health services research. prereq: [Grad or professional school] student or instr consent

STAT 5101 - Theory of Statistics I

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

Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

STAT 5302 - Applied Regression Analysis

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

Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.

STAT 5303 - Designing Experiments

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

Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent

STAT 5401 - Applied Multivariate Methods

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

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

STAT 5511 - Time Series Analysis

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

Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102

STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods

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

Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent

STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling

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

Design experiments/analyze data with fixed effects, random/mixed effects models. ANOVA for factorial designs. Contrasts, multiple comparisons, power/sample size, confounding, fractional factorials. Computer-generated designs. Response surfaces. Multi-level models. Generalized estimating equations (GEE) for longitudinal data with non-normal errors. prereq: 8051 or instr consent

STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression

Credits:	3.0 [max 3.0]
Prerequisites:	PhD student in stat or DGS permission and 8052
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation. Topics in advanced regression. prereq: PhD student in stat or DGS permission and 8052

HINF 5496 - Internship in Health Informatics

Credits:	1.0 -6.0 [max 18.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

Practical industrial experience not directly related to student's normal academic experience. prereq: HINF 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 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

HINF 8220 - Computational Causal Analytics

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

Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.

HINF 8492 - Advanced Readings or Research in Health Informatics

Credits:	1.0 -6.0 [max 24.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall, Spring & Summer

Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent

HINF 5431 - Foundations of Health Informatics II

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

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 8431 - Foundations of Health Informatics II Lab

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

The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.

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 5620 - Data Visualization for the Health Sciences

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

An advanced health informatics course, focusing on theoretical and practical aspects of data and information visualization for health care and the health sciences. Topics include classic and novel visualization types; models of human visual perception and cognition; color, text and typography; maps and diagrams; evaluation and testing; and the aesthetic and cultural aspects of visualization. Examples emphasize health sciences applications for clinicians, patients, researchers, and analysts. Modern programming and commercial tools are discussed, including D3, ggplot2, and Tableau. Students will report on and discuss visualization methods, published studies and books, culminating in a final visualization project of the student's choosing.

MATH 5467 - Introduction to the Mathematics of Image and Data Analysis

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

Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended

NURS 7113 - Clinical Decision Support: Theory

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

Principles and concepts of knowledge management and decision making for support of clinical practice. Students design a clinical decision support intervention and examine the legal, ethical, and practical issues related to its implementation and maintenance of CDS interventions. prereq: 5115 or HINF 5430/5431 or instr consent

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 6560 - Operations Research and Quality in Health Care

Credits:	3.0 [max 3.0]
Prerequisites:	Grad-level statistics/management coursework
Grading Basis:	A-F only
Typically offered:	Every Fall

Using a systems perspective to develop models to analyze/improve health care operations. Identifying data needs/sources to model structures, processes, and outcomes of care. Applying quality improvement, management sciences/operations research techniques to real world health care problems. prereq: Grad-level statistics/management coursework

PUBH 6717 - Decision Analysis for Health Care

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

Introduction to methods/range of applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation.

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 6765 - Continuous Quality Improvement: Methods and Techniques

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

Theory/practical applications of concepts, tools, techniques of continuous quality improvement (QI) in public health/health care.

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 6862 - Cost-Effectiveness Analysis in Health Care

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

Government regulations. New technologies. Diagnosis/treatment protocols. Strengths, limitations, appropriateness of different approaches. prereq: instr consent; introductory econ course recommended

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 8452 - Advanced Longitudinal Data Analysis

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

Methods of inference for outcome variables measured repeatedly in time or space. Linear/nonlinear models with either normal or non-normal error structures. Random effects. Transitional/marginal models with biomedical applications. prereq: [Stat 5102, Stat 8311, experience with [SAS or S+], advanced [biostats or stat] student] or instr consent

PUBH 8462 - Advanced Survival Analysis

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

Statistical methods for counting processes. Martingale theory (transforms, predictable processes, Doob decomposition, convergence, submartingales). Applications to nonparametric intensity estimation. Additive/relative risk models. Inference for event history data, recurrent events, multivariate survival, diagnostics. prereq: [7450, 8432, Stat 5102, advanced [biostatistics or statistics] major] or instr consent

PUBH 8472 - Spatial Biostatistics

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

Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended

STAT 5101 - Theory of Statistics I

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

Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

STAT 5102 - Theory of Statistics II

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

Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]

STAT 5302 - Applied Regression Analysis

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

Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.

STAT 5511 - Time Series Analysis

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

Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102

STAT 5401 - Applied Multivariate Methods

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

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods

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

Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent

PUBH 7440 - Introduction to Bayesian Analysis

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

Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent

CSCI 5521 - Machine Learning Fundamentals

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

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives

CSCI 5523 - Introduction to Data Mining

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

Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent

CSCI 5525 - Machine Learning: Analysis and Methods

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent

PUBH 8475 - Statistical Learning and Data Mining

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 8475/ Stat 8056
Typically offered:	Periodic Spring

Statistical techniques for extracting useful information from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging/boosting), unsupervised learning. prereq: [[[6450, 6451, 6452] or STAT 5303 or equiv], [biostatistics or statistics PhD student]] or instr consent

CSCI 5105 - Introduction to Distributed Systems

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

Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling. prereq: [5103 or equiv] or instr consent

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming

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

Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 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

CSCI 5521 - Machine Learning Fundamentals

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

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives

CSCI 5421 - Advanced Algorithms and Data Structures

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

Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent

HINF 8220 - Computational Causal Analytics

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

Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.

HINF 5650 - Integrative Genomics and Computational Methods

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

Genome-scale high throughput data sets are a central feature of modern biological research and translational clinical study. Experimental, computational biologists and clinical researchers who want to get the most from their data sets need to have a firm grasp and understanding of genomic data structure characteristics, analytical methodology and the intrinsic connection to integrate. This course is designed to build competence in quantitative methods for the analysis of high-throughput genomic data and data integration.

STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods

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

Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent

STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling

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

Design experiments/analyze data with fixed effects, random/mixed effects models. ANOVA for factorial designs. Contrasts, multiple comparisons, power/sample size, confounding, fractional factorials. Computer-generated designs. Response surfaces. Multi-level models. Generalized estimating equations (GEE) for longitudinal data with non-normal errors. prereq: 8051 or instr consent

BIOC 8007 - Molecular Biology of the Genome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8008 - Molecular Biology of the Transcriptome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

HINF 5496 - Internship in Health Informatics

Credits:	1.0 -6.0 [max 18.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent

HINF 8492 - Advanced Readings or Research in Health Informatics

Credits:	1.0 -6.0 [max 24.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall, Spring & Summer

Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent

HINF 5431 - Foundations of Health Informatics II

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

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 8431 - Foundations of Health Informatics II Lab

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

The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.

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 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

MEDC 5245 - Introduction to Drug Design

Credits:	3.0 [max 3.0]
Course Equivalencies:	Chem 5245/Phar 6245/MedC 5245
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Concepts that govern design/discovery of drugs. Physical, bioorganic, medicinal chemical principles applied to explain rational design, mechanism of action drugs. prereq: Chem

PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine

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

This course is for individuals wanting advanced knowledge in PGx and an introduction to the broader field of precision medicine. It consists of lectures, homework assignments and class discussions designed to introduce precision medicine as it relates to the impact of genetic variation on health and the advanced practice of pharmacogenomics. Students will learn diagnostic methods to identify germline and somatic mutations, how algorithms and equations are used for predictions and the relationships between genes and environment. How these concepts apply in the clinical settings, forecast health and drug response will be presented. Emerging tools in genetics and pharmacogenomics will be presented. The course will also address clinical implementation and the ethical, legal, and social issues presented by precision medicine and PGx. Prerequisite: 3rd year PharmD student, graduate student in healthcare or related program, or equivalent experience or instructor consent

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

PUBH 8445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 7445/PubH 8445
Typically offered:	Fall Odd Year

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: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable

STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression

Credits:	3.0 [max 3.0]
Prerequisites:	PhD student in stat or DGS permission and 8052
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation. Topics in advanced regression. prereq: PhD student in stat or DGS permission and 8052

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 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 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety

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

Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. prereq: Junior or senior or grad student or professional student or instr consent

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 7402 - Biostatistics Modeling and Methods

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

Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences

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 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

HINF 5496 - Internship in Health Informatics

Credits:	1.0 -6.0 [max 18.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent

HINF 8492 - Advanced Readings or Research in Health Informatics

Credits:	1.0 -6.0 [max 24.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Fall, Spring & Summer

Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent

PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine

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

This course is for individuals wanting advanced knowledge in PGx and an introduction to the broader field of precision medicine. It consists of lectures, homework assignments and class discussions designed to introduce precision medicine as it relates to the impact of genetic variation on health and the advanced practice of pharmacogenomics. Students will learn diagnostic methods to identify germline and somatic mutations, how algorithms and equations are used for predictions and the relationships between genes and environment. How these concepts apply in the clinical settings, forecast health and drug response will be presented. Emerging tools in genetics and pharmacogenomics will be presented. The course will also address clinical implementation and the ethical, legal, and social issues presented by precision medicine and PGx. Prerequisite: 3rd year PharmD student, graduate student in healthcare or related program, or equivalent experience or instructor consent

HINF 5431 - Foundations of Health Informatics II

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

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

MATH 5652 - Introduction to Stochastic Processes

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

Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101

MATH 5445 - Mathematical Analysis of Biological Networks

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

Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations

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 7440 - Introduction to Bayesian Analysis

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

Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent

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 8432 - Probability Models for Biostatistics

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

Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent

PUBH 8442 - Bayesian Decision Theory and Data Analysis

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

Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent

PUBH 8445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 7445/PubH 8445
Typically offered:	Fall Odd Year

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: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable

PUBH 8446 - Advanced Statistical Genetics and Genomics

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

Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent

STAT 5511 - Time Series Analysis

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

Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102

STAT 5401 - Applied Multivariate Methods

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

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

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Health Informatics Ph.D.