Twin Cities campus
 
Twin Cities Campus

Health Informatics M.S.

Health Informatics, AHC Inst
Graduate School
Link to a list of faculty for this program.
Contact Information
Physical Address: 8-100 PWB, 516 Delaware St. SE, Minneapolis, MN 55455 Mailing Address: MMC 912, 420 Delaware St. SE, Minneapolis, MN 55455
Email: ihi@umn.edu
  • Program Type: Master's
  • Requirements for this program are current for Spring 2019
  • Length of program in credits: 36
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
Health informatics (also known as biomedical informatics) is an interdisciplinary field of scholarship that applies computer, information, statistical, management, and related scientific methods to enable biomedical discovery and support the effective and efficient use and analysis of data, management of information, and application of knowledge across the spectrum from basic science to clinical care. The ultimate goal of the field is to improve the health, well-being, and economic functioning of society. Students take a sequence of core courses in health informatics, computing, and biostatistics, and electives in technical and health science areas. Possible areas of emphasis include health information systems, telehealth, bioinformatics, user interface design, system impact evaluation, database construction and analysis, clinical decision-making, evaluation of health programs, and physiological monitoring and control. The health informatics MS is intended for students who are interested in research, but who do not have the background or are not ready to commit to the PhD program.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
  • partially online (between 50% to 80% of instruction is online)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.30.
Applicants are expected to have at least a bachelor of science or equivalent degree from a regionally accredited institution of higher education.
Required prerequisites
Health or Biological Sciences
Applicants must have taken 6 semester-credits or 9 quarter-credits at the undergraduate or graduate level in medical, life, or biological sciences from a regionally accredited institution of higher learning or equivalent. This broadly defined requirement includes most courses with a health or biology emphasis, including biostatistics, health services research, and public health, as well as more traditional biology or life science courses.
Programming Language
Documented work or educational experience working with a programming language such as C, C++, Java, Python, R, Visual Basic, etc.
or HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
or Department Consent
Applicants must submit their test score(s) from the following:
  • GRE
    • General Test - Verbal Reasoning: 151
    • General Test - Quantitative Reasoning: 160
    • General Test - Analytical Writing: 4.0
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
  • IELTS
    • Total Score: 6.5
    • Reading Score: 6.5
    • Writing Score: 6.5
  • MELAB
    • Final score: 80
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (GRE, TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan A: Plan A requires 26 major credits, 0 credits outside the major, and 10 thesis credits. The final exam is written and oral.
Plan B: Plan B requires 36 major credits and 0 credits outside the major. The final exam is written and oral.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 1 semesters must be completed before filing a Degree Program Form.
Required HINF Courses (16 credits)
All students must take AHC Informatics Grand Rounds (HINF 5436) twice for a total of two credits.
HINF 5430 - Foundations of Health Informatics I (3.0 cr)
HINF 5431 - Foundations of Health Informatics II (3.0 cr)
HINF 5436 - AHC Informatics Grand Rounds (1.0 cr)
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation (3.0 cr)
HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety (2.0 cr)
HINF 5531 - Health Data Analytics and Data Science (3.0 cr)
Other Required Courses (7 credits)
NURS 5116 - Consumer Health Informatics (1.0 cr)
NURS 7108 - Population Health Informatics (2.0 cr)
PUBH 6450 - Biostatistics I (4.0 cr)
Electives
Plan A students must take at least 3 credits, and Plan B students must take at least 9 credits of electives to meet the 36-credit minimum. If labs or practicums are selected as electives, they must be taken concurrently with the associated course (i.e. take HINF 8430 with HINF 5430). Electives must be approved by the advisor.
BIOC 5361 - Microbial Genomics and Bioinformatics (3.0 cr)
BIOC 8007 - Molecular Biology of DNA (2.0 cr)
BIOC 8008 - Molecular Biology of RNA (2.0 cr)
CGSC 8410 - Perspectives in Learning, Perception, and Cognition (2.0 cr)
CSCI 5106 - Programming Languages (3.0 cr)
CSCI 5115 - User Interface Design, Implementation and Evaluation (3.0 cr)
CSCI 5271 - Introduction to Computer Security (3.0 cr)
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics (3.0 cr)
CSCI 5481 - Computational Techniques for Genomics (3.0 cr)
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5521 - Introduction to Machine Learning (3.0 cr)
CSCI 5525 - Machine Learning (3.0 cr)
CSCI 5607 - Fundamentals of Computer Graphics 1 (3.0 cr)
CSCI 5608 - Fundamentals of Computer Graphics II (3.0 cr)
CSCI 5707 - Principles of Database Systems (3.0 cr)
CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
CSCI 5801 - Software Engineering I (3.0 cr)
CSCI 8725 - Databases for Bioinformatics (3.0 cr)
DES 5185 - Human Factors in Design (3.0 cr)
EPSY 5244 - Survey Design, Sampling, and Implementation (3.0 cr)
EPSY 5262 - Intermediate Statistical Methods (3.0 cr)
EPSY 5621 - Assessment and Instructional Design for Students with Developmental Disabilities (3.0 cr)
GCD 8103 - Human Histology (5.0 cr)
HINF 5440 - Foundations of Translational Bioinformatics (3.0 cr)
HINF 5450 - Foundations of Precision Medicine Informatics (3.0 cr)
HINF 5494 - Topics in Health Informatics (3.0 cr)
HINF 5496 - Internship in Health Informatics (1.0-6.0 cr)
HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
HINF 5610 - Foundations of Biomedical Natural Language Processing (3.0 cr)
HINF 5620 - Data Visualization for the Health Sciences (3.0 cr)
HINF 5630 - Clinical Data Mining (3.0 cr)
HINF 5640 - Advanced Translational Bioinformatics Methods (3.0 cr)
HINF 5650 - Integrative Genomics and Computational Methods (3.0 cr)
HINF 8220 - Computational Causal Analytics (3.0 cr)
HINF 8405 - Advanced Topics in Health Informatics I (1.0-4.0 cr)
HINF 8406 - Advanced Topics in Health Informatics II (1.0-4.0 cr)
HINF 8430 - Foundations of Health Informatics I Lab (2.0 cr)
HINF 8431 - Foundations of Health Informatics II Lab (2.0 cr)
HINF 8440 - Foundations of Translational Bioinformatics Lab (2.0 cr)
HINF 8492 - Advanced Readings or Research in Health Informatics (1.0-6.0 cr)
HINF 8525 - Health Informatics Teaching (2.0 cr)
HINF 8535 - Advanced Health Informatics Research Methods (3.0 cr)
IDSC 6040 - Information Technology Management (2.0 cr)
IDSC 6050 - Information Technologies and Solutions (2.0 cr)
IDSC 6471 - Knowledge Management (2.0 cr)
IDSC 8721 - Behavioral Decision Theory (3.0 cr)
IE 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
KIN 5001 - Foundations of Human Factors/Ergonomics (3.0 cr)
LING 5001 - Introduction to Linguistics (4.0 cr)
LING 5205 - Semantics (3.0 cr)
LING 5801 - Introduction to Computational Linguistics (3.0 cr)
MATH 5445 - Mathematical Analysis of Biological Networks (4.0 cr)
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
MEDC 5245 - Introduction to Drug Design (3.0 cr)
MILI 6992 - Healthcare Delivery Innovations:Optimizing Cost and Quality (2.0 cr)
MILI 6995 - Medical Industry Valuation Laboratory (2.0 cr)
NURS 5115 - Interprofessional Health Care Informatics (3.0 cr)
NURS 5117 - Consumer Health Informatics Practicum (1.0 cr)
NURS 6105 - Systems Analysis and Design (3.0 cr)
NURS 7106 - Knowledge Representation and Interoperability Practicum (2.0 cr)
NURS 7109 - Population Health Informatics Practicum (2.0 cr)
NURS 7113 - Clinical Decision Support: Theory (2.0 cr)
NURS 7114 - Clinical Decision Support Practicum (2.0 cr)
NURS 7118 - Human Factors and Human-Computer Interaction in Health Informatics (3.0 cr)
NURS 7610 - System Leadership and Innovation (3.0 cr)
PHAR 6224 - Pharmacogenomics: Genetic Basis for Variability in Drug Response (2.0 cr)
PUBH 6020 - Fundamentals of Social and Behavioral Science (2.0 cr)
PUBH 6025 - Designing e-Interventions for Public Health (2.0 cr)
PUBH 6102 - Issues in Environmental Health (2.0 cr)
PUBH 6131 - Working in Global Health (2.0 cr)
PUBH 6320 - Fundamentals of Epidemiology (3.0 cr)
PUBH 6325 - Data Processing with PC-SAS (1.0 cr)
PUBH 6341 - Epidemiologic Methods I (3.0 cr)
PUBH 6386 - Public Health Aspects of Cardiovascular Disease (2.0 cr)
PUBH 6420 - Introduction to SAS Programming (1.0 cr)
PUBH 6541 - Statistics for Health Management Decision Making (3.0 cr)
PUBH 6555 - Topics in Health Economics (2.0 cr)
PUBH 6556 - Health and Health Systems (3.0 cr)
PUBH 6557 - Health Finance I (3.0 cr)
PUBH 6558 - Health Finance II (3.0 cr)
PUBH 6560 - Operations Research and Quality in Health Care (3.0 cr)
PUBH 6562 - Information Technology in Health Care (2.0 cr)
PUBH 6564 - Private Purchasers of Health Care: Roles of Employers and Health Plans in U.S. Health Care System (2.0 cr)
PUBH 6565 - Innovation of Healthcare Services (2.0 cr)
PUBH 6617 - Practical Methods for Secondary Data Analysis (3.0 cr)
PUBH 6717 - Decision Analysis for Health Care (2.0 cr)
PUBH 6724 - The Health Care System and Public Health (3.0 cr)
PUBH 6742 - Ethics in Public Health: Research and Policy (1.0 cr)
PUBH 6751 - Principles of Management in Health Services Organizations (2.0 cr)
PUBH 6765 - Continuous Quality Improvement: Methods and Techniques (3.0 cr)
PUBH 6780 - Topics: Public Health Administration and Policy (1.0-3.0 cr)
PUBH 6800 - Topics: Health Services Research and Policy (0.5-4.0 cr)
PUBH 6802 - Managing Electronic Health Information (3.0 cr)
PUBH 6803 - Conducting a Systematic Literature Review (3.0 cr)
PUBH 6809 - Advanced Methods in Health Decision Science (3.0 cr)
PUBH 6814 - Data and Information for Population Health Management (2.0 cr)
PUBH 6832 - Economics of the Health Care System (3.0 cr)
PUBH 6862 - Cost-Effectiveness Analysis in Health Care (3.0 cr)
PUBH 6863 - Understanding Health Care Quality (2.0 cr)
PUBH 6876 - Public Health Systems Analysis and Design (2.0 cr)
PUBH 7400 - Topics: Biostatistics (0.5-4.0 cr)
PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
PUBH 7405 - Biostatistics: Regression (4.0 cr)
PUBH 7407 - Analysis of Categorical Data (3.0 cr)
PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 7460 - Advanced Statistical Computing (3.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
PUBH 7588 - Information Uses in Long-Term Care (2.0 cr)
PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 8446 - Advanced Statistical Genetics and Genomics (3.0 cr)
PUBH 8452 - Advanced Longitudinal Data Analysis (3.0 cr)
PUBH 8462 - Advanced Survival Analysis (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
PUBH 8801 - Health Services Policy Analysis: Theory (3.0 cr)
PUBH 8810 - Research Studies in Health Care (3.0 cr)
STAT 5101 - Theory of Statistics I (4.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5303 - Designing Experiments (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling (3.0 cr)
STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression (3.0 cr)
Plan Options
Plan A
Take at least 10 master's thesis credits.
HINF 8777 - Thesis Credits: Master's (1.0-18.0 cr)
-OR-
Plan B
Take the following course:
HINF 8770 - Plan B Project (4.0 cr)
Program Sub-plans
A sub-plan is not required for this program.
Students may not complete the program with more than one sub-plan.
 
More program views..
· Graduate School

View PDF Version:
Search.
Search Programs

Search University Catalogs
Related links.

Graduate School

Graduate Admissions

Graduate School Fellowships

Graduate Assistantships

Colleges and Schools

One Stop
for tuition, course registration, financial aid, academic calendars, and more
 
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 5430 - Foundations of Health Informatics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 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 5436 - AHC Informatics Grand Rounds
Credits: 1.0 [max 10.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Presentation/discussion of research problems, current literature/topics of interest in Health Informatics.
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]
Grading Basis: A-F or Aud
Typically offered: Every Fall & 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: 1.0 [max 1.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Examines issues from consumer's perspective in acquisition, understanding, use or provision of health information. Online strategies for improving health. Impact on consumer-provider relationships/ethical and legal issues. 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
Descriptive statistics. Gaussian probability models, point/interval estimation for means/proportions. Hypothesis testing, including t, chi-square, and nonparametric tests. Simple regression/correlation. ANOVA. Health science applications using output from statistical packages. prereq: [College-level algebra, 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 DNA
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Structure and organization of genes. Replication. Transcription. Epigenetic modification of chromatin. Genome editing. Deep sequencing.Cellular adhesion mechanisms.
BIOC 8008 - Molecular Biology of RNA
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Translation. RNA editing. Epigenetics and long non-coding RNA. MicroRNAs and RNA interference. Pre-mRNA processing.
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 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: 01666
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 - Introduction to Machine Learning
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] or instr consent
CSCI 5525 - Machine Learning
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 4707/5707/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: 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: Periodic Fall
Theories/methods that influence the assessment of physical, social, and psychological human factors. Development of user needs with application to designed products that interact with human body. prereq: Grad student or sr or instr consent
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
EPSY 5621 - Assessment and Instructional Design for Students with Developmental Disabilities
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Methods/materials course. Functional/standards-based approaches to promoting academic learning in students with developmental disabilities. prereq: 5613, 5614
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 5440 - Foundations of Translational Bioinformatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics
HINF 5450 - Foundations of Precision Medicine Informatics
Credits: 3.0 [max 3.0]
Typically offered: Every 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: 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: A-F or Aud
Typically offered: Every 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: Every 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 Fall
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 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 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 8440 - Foundations of Translational Bioinformatics Lab
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 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 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 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 6040 - Information Technology Management
Credits: 2.0 [max 2.0]
Course Equivalencies: 00760 - IDSc 6040/MBA 6240
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 6050 - 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: Every 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: 00099 - 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
Healthcare delivery. Analyze value for population.
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 - Interprofessional Health Care Informatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Implications of informatics for practice, including nursing, public health, and health care in general. Electronic health record issues. Ethical, legislative, political, and global/future informatics issues.
NURS 5117 - Consumer Health Informatics Practicum
Credits: 1.0 [max 1.0]
Grading Basis: S-N only
Typically offered: Every Fall
Apply student knowledge to analysis of health needs and consumer health principles, theories, and research to a consumer health informatics project. prereq: [Grad student, [5116 or concurrent registration is required (or allowed) in 5116]] or instr 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 - System Leadership and Innovation
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Integrate whole system thinking, contemporary theories, and evidence of factors contributing to effective leadership to advance innovation and achieve sustainable change in contemporary health care environments.
PHAR 6224 - Pharmacogenomics: Genetic Basis for Variability in Drug Response
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Theory/practice of pharmacogenomics. Principles of human genetics/genomics. Applications to scientific education, problems in drug therapy optimization, patient care. prereq: At least 3rd year or later in healthcare or related program or equivalent experience or instr 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
Four major approaches to public health problems: psychosocial, economic, community, policy. Theory, implementation. Small groups practice skills.
PUBH 6025 - Designing e-Interventions for Public Health
Credits: 2.0 [max 2.0]
Typically offered: Spring Even Year
Provide an overview of how technology may be used as a recruitment, assessment, and intervention tool in public health research and practice.
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: 02236 - 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 - Public Health Aspects of Cardiovascular Disease
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
Well-established risk factors for cardiovascular disease (CVD), prevention of CVD, and national recommendations for treatment/prevention. Emerging risk factors, current controversies in CVD. prereq: [PubH 6341 AND 6450] OR [equivalent] OR [permission of instructor]
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 - Topics in 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: [[Health care admin or PubH admin/policy] student, familiarity with computerized spread-sheets]] 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 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.
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 6617 - Practical Methods for Secondary Data Analysis
Credits: 3.0 [max 3.0]
Prerequisites: Graduate level student or #
Typically offered: Every Fall
Introduction to methods for finding, transferring, and processing existing data sources. Focuses on practical approaches to pre-statistical data processing and analysis with STATA using a PC with an MS Windows operating system. Complex survey samples, other survey biases. prereq: Graduate level student or instr consent
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. prereq: Public health [MPH or certificate] student or [clinical research MS or Environmental health [MS or PhD] or epidemiology PhD or [health services research, policy/admin [MS or PhD]] major or instr consent
PUBH 6751 - Principles of Management in Health Services Organizations
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Understanding of and improvement in the competencies of managers in organizations, particularly as applied to health services and public health organizations. prereq: [Public hlth MPH or MHA or certificate] student or [environmental health MS or PhD] student or dentistry MS student or instr consent
PUBH 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: Public Health Administration and Policy
Credits: 1.0 -3.0 [max 60.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
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 6802 - Managing Electronic Health Information
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Organizational processes affecting quality, consistency, completeness, accuracy, organization, use of health information. Using relational data theory to describe structure of information. Using SQL/SAS to create/query databases. prereq: Familiarity with [Windows-based environment, Microsoft Word, Excel, Access, Web browser, graphical package, data collection/analysis projects, internet] or instr consent
PUBH 6803 - Conducting a Systematic Literature Review
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Developing skills built on evidence-based practice. Draws on staff of Minnesota Evidence-based Practice Center. prereq: Basic knowledge of 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 6814 - Data and Information for Population Health Management
Credits: 2.0 [max 2.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Information is used for managing population health surveillance, profiling providers, measuring quality, measuring resource use, and managing population health. This course describes the organizational context of health information and how to use health data to manage population health. Sources and types of health information, organizational processes affecting information quality, consistency, completeness, and accuracy, methods for organizing information, use of information for decision making, and how data can be used to provide usable information, will be discussed. prereq: Completion or concurrent enrollment in PubH 6813, Managing Electronic Health Information, 2cr contains the skills necessary for completing the assigned paper/project in this course, PubH 6814, OR instructor permission.
PUBH 6832 - Economics of the Health Care System
Credits: 3.0 [max 3.0]
Course Equivalencies: 00293 - Econ 5890/PubH 5893
Typically offered: Every Fall
Development of traditional issues in health economics. Production of health, demand for health capital and health care, insurance theory and markets, managed care, pricing, physician's services, production and costs in health care institutions, role of government, cost effectivness analysis, reform. prereq: [[Grad or professional school] student, knowledge of [microeconomic analytical tools, analytical statistics]] or instr consent
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 6876 - Public Health Systems Analysis and Design
Credits: 2.0 [max 2.0]
Grading Basis: OPT No Aud
Typically offered: Every Fall
Basic knowledge/skills to design, develop, implement public health information systems. Systems development lifecycle, including problem definition, feasibility analysis, logical modeling, system architecture/implementation. Develop communication, analysis, management skills needed to develop information systems that meet user needs. prereq: Grad or professional student or instr consent
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 - Biostatistics: Regression
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]
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.
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: 01183
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: [[[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: 3.0 [max 3.0]
Typically offered: Every Fall
Integrated overview of U.S. health services policy. Related theoretical/empirical literature. Analysis of alternative policy-making models, political/philosophical underpinnings of those models. prereq: [Grad or professional school] student 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]
Course Equivalencies: 00259 - Math 5651/Stat 5101
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], [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: 3022 or 4102 or 5021 or 5102 or instr consent
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 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.
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: Theoretical understanding
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 8777 - Thesis Credits: Master's
Credits: 1.0 -18.0 [max 50.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
(No description) prereq: Max 18 cr per semester or summer; 10 cr total required [Plan A only]
HINF 8770 - Plan B Project
Credits: 4.0 [max 4.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Research project. Topic arranged between student/instructor. Written report required. prereq: Advanced plan B MS student
HINF 5430 - Foundations of Health Informatics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 5436 - AHC Informatics Grand Rounds
Credits: 1.0 [max 10.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Presentation/discussion of research problems, current literature/topics of interest in Health Informatics.
BIOC 8007 - Molecular Biology of DNA
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Structure and organization of genes. Replication. Transcription. Epigenetic modification of chromatin. Genome editing. Deep sequencing.Cellular adhesion mechanisms.
BIOC 8008 - Molecular Biology of RNA
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Translation. RNA editing. Epigenetics and long non-coding RNA. MicroRNAs and RNA interference. Pre-mRNA processing.
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 5525 - Machine Learning
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
HINF 5440 - Foundations of Translational Bioinformatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 5650 - Integrative Genomics and Computational Methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every 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 Fall
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 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 8440 - Foundations of Translational Bioinformatics Lab
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 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
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
HINF 8770 - Plan B Project
Credits: 4.0 [max 4.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Research project. Topic arranged between student/instructor. Written report required. prereq: Advanced plan B MS student
HINF 5430 - Foundations of Health Informatics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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 5436 - AHC Informatics Grand Rounds
Credits: 1.0 [max 10.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Presentation/discussion of research problems, current literature/topics of interest in Health Informatics.
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]
Grading Basis: A-F or Aud
Typically offered: Every Fall & 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 5440 - Foundations of Translational Bioinformatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics
HINF 5450 - Foundations of Precision Medicine Informatics
Credits: 3.0 [max 3.0]
Typically offered: Every 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 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 8440 - Foundations of Translational Bioinformatics Lab
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
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
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: A-F or Aud
Typically offered: Every 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 - Pharmacogenomics: Genetic Basis for Variability in Drug Response
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Theory/practice of pharmacogenomics. Principles of human genetics/genomics. Applications to scientific education, problems in drug therapy optimization, patient care. prereq: At least 3rd year or later in healthcare or related program or equivalent experience or instr consent
HINF 8770 - Plan B Project
Credits: 4.0 [max 4.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Research project. Topic arranged between student/instructor. Written report required. prereq: Advanced plan B MS student