Health Informatics
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Graduate Programs
Description
Effectively use health informatics and analytics to impact health, health promotion, healthcare delivery, and healthcare decision making by preparing healthcare professionals, analysts, and visionary future leaders who maximize inter-professional collaborations through data analysis, knowledge discovery, and dissemination of cutting edge innovations for the benefit of the individual, family, and business while promoting societal health outcomes.
Majors |
Program | Locations | Total Credits |
|---|---|---|---|
| Health Informatics Analytics PSM | PSM - Professional Science Master's | 31 |
Policies & Faculty
Policies
Admission Requirements
(see separate section for program specific admission policy)
Grades, Progression, and Retention
The student has rights which must be protected. These rights include, but are not limited to: fair evaluations, advisement and counseling, and assistance in identifying and meeting learning goals.
- Goal-Directed Behaviors
Each student is expected to demonstrate and maintain goal directed, professional behavior (e.g. identification of personal learning objectives which are in line with course outcomes, maintaining personal responsibility for achievement of learning objectives, and demonstrating collaborative behavior regarding teaching-learning activities). - Drop/Add
The HIA PSM adheres to the University policy of drop/add dates for complete session courses. Students are expected to follow the dates as printed in the class schedule. - Grade Requirements
Once provisionally or fully admitted to the HIA PSM, the student must achieve at least a “B” (3.0) in each required course. Courses in the curriculum are sequentially arranged and progression is based on successful completion of the prerequisite course(s).
Graduation Requirements
In addition to the graduation requirements of the College of Graduate Studies and Research, students in this program must also satisfactorily complete the capstone seminar course (IT692 or NURS692) demonstrating an appropriate level of mastery of each of the program level outcomes.
Depending on whether the student is currently employed in the field, the capstone experience can be performed for the current employer, as part of an internship, externship, or to benefit a non-profit entity, or as a project designed in connection with an mock, open source, or appropriately obtained proprietary data set. Each student must make a presentation to faculty and peers in the program. Where necessary, intellectual property waivers and/or non-disclosure agreements will need to be in place ahead of such presentation.
All courses counted as part of degree completion must have grades of a B or better (3.0 on 4.0 point scale).
Contact Information
500 Level
Credits: 4
Students will gain foundational skills in health informatics, systems analysis, data modeling, data gathering, data retrieval, data governance and systems security to create useful information for health-related decision making. This course does not count toward the MS IT degree.Prerequisites: none
Credits: 4
Concepts and algorithms used in computer graphics, including polygonal and curved images in both 2 and 3 dimensions, representation of solid objects, and color and illumination models. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
This course endeavors to provide the student with a solid understanding of the principles, techniques and tools involved in advanced object-oriented programming as it is practiced in enterprise industries. The successful student should have a distinct advantage in the marketplace. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
This course provides an introduction to data science, discusses opportunities and challenges associated with data science projects, and develops competencies related to data collection, data cleaning, data analysis, and model evaluation. The course focuses on hands-on exercises using data analytics tools.Prerequisites: none
Credits: 4
Current practice and future directions in robotics, including robot anatomy, kinematics, sensors, sensor interfacing and fusion, mobile robotics, real-time programming, vision and image processing algorithms, and subsumption architecture. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
Extensive coverage of SQL, database programming, large scale data modeling, and database enhancement through reverse engineering. This course also covers theoretical concepts of query processing, and optimization, basic understanding of concurrency control and recovery, and database security and integrity in centralized/distributed environments. Team-oriented projects in a heterogeneous client server environment.Prerequisites: none
Credits: 4
This course provides science and study of methods of protecting data, and designing disaster recovery strategy. Secure database design, data integrity, secure architectures, secure transaction processing, information flow controls, inference controls, and auditing. Security models for relational and object-oriented databases. Pre: With permission by instructor.Prerequisites: none
Credits: 4
The course explores big data in structured and unstructured data sources. Emphasis is placed on big data strategies, techniques and evaluation methods. Various data analytics are covered. Students experiment with big data through big data analytics, data mining, and data warehousing tools.Prerequisites: none
Credits: 4
The course includes information warfare principles and technologies. The key areas are: Information warfare concepts; Protocols, Authentication, and Encryption; Network attach techniques, methodologies, and tools; Network defense; Malware: trojans, worms, viruses, and malicious code; Electronic crimes and digital evidence. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
This course examines the organizational leadership structure and competencies of healthcare and/or IT organizations, the governance planning process, financial management, ethical and legal decision-making, privacy, and data-based best practices that balance organizational and regulatory requirements with feasible cost-effective solutions.Prerequisites: none
Credits: 4
Advanced coverage of data communication, networking and security protocols. Topics include: data transmission methods, error detection and recovery, flow control, routing, data throughput, security issues, and performance analysis of existing and emerging protocols for secure communication between the many points within a computer network and across the internet. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
Network and server systems administration include: domain administration; file system management; networked printers; user management; and workstation configuration. Network programming experience will be gained through programming assignments/projects in Layered Software Systems, HTTP Server, UDP (TFTP or DNS), CGI program, IPV6, RPC/SCTP. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
This course provides an understanding of existing and emerging mobile and wireless data networks, with an emphasis on digital data communications. Students will gain an understanding of the unique considerations that must be given to network protocols for wireless and mobile communication as well as their applications. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
This course is designed to give students the skills required to write applications for mobile devices (smartphones and tablets). Topics to be covered include interacting with the UI, using an emulator/simulator, application lifecycle, moving from one screen to another, services, alarms, broadcast receivers, maps API, location based programs, gps, persistence, hardware sensors, and web applications.Prerequisites: none
Credits: 4
Topics include software quality assurance, software quality metrics, software configuration management, software verification and validation, reviews, inspections, and software process improvement models, functional and structural testing models.Prerequisites: none
Credits: 4
This course discusses concepts and techniques for design, development and evaluation of user interfaces. Students will learn the principles of interaction design, interaction styles, user-centered design, usability evaluation, input/output devices, design and analysis of controlled experiments and principles of perception and cognition used in building efficient and effective interfaces. Group project work.Prerequisites: none
Credits: 4
HTTP Protocol; Presentation abstractions; Web-markup languages; Client-side programming; Server-side programming; Web services; Web servers; Emerging technologies; Security; Standards & Standard Bodies; Techniques for web interface design; User-centered design; Visual development environments and development tools; Measure the effectiveness of interface design. Pre: With permission by the instructor.Prerequisites: none
Credits: 4
An introduction to all important aspects of software engineering. The emphasis is on principles of software engineering including project planning, requirements gathering, size and cost estimation, analysis, design, coding, testing, implementation, and maintenance. Group project work.Prerequisites: none
Credits: 4
This course is designed to give students the skills required to design and develop video games. The primary focus of the course is on mobile game development, game design principles and user-centered design methodologies. A play-centric approach to game design and development will be studied, discussed and applied in the production of a game demo.Prerequisites: none
Credits: 1-4
Special topics not covered in other courses. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1-12
Provides students with opportunity to utilize their training in a real-world business environment working under the guidance and direction of a faculty member. (A maximum of 4 credits apply toward a degree in this department.) Pre: consent Fall, Spring, SummerPrerequisites: none
600 Level
Credits: 3
Research methodology in general and in computer science. Data and research sources. Analysis of existing research. Preliminary planning and proposals. Conceptualization, design, and interpretation of research. Good reporting. Same as CS 600. Pre-req: An elementary statistics course.Prerequisites: none
Credits: 3
Special topics in computer science research not covered in other courses. May be repeated for credit on each new topic.Prerequisites: none
Credits: 1
Students attend seminar presentations and present a research topic at one of the seminars. Same as CS 602. Pre-req: consentPrerequisites: none
Credits: 3
This course is a continuation of Artificial Intelligence (IT 530). Emphasis is placed on advanced topics and the major areas of current research within the field. Theoretical and practical issues involved with developing large-scale systems are covered. Same as CS 630. Pre-req: IT 530Prerequisites: none
Credits: 3
The design of large-scale, knowledge¿based data mining. Emphasis on concepts and application of machine learning using big data. Examination of knowledge representation techniques and problem¿solving methods used to design knowledge¿based systems. Pre-req: instructor permission requiredPrerequisites: none
Credits: 3
In-depth study of advanced topics such as object-oriented databases, intelligent database systems, parallel databases, database mining and warehousing, distributed database design and query processing, multi-database integration and interoperability, and multilevel secure systems.Prerequisites: none
Credits: 3
In this course, students will design and implement distributed big data architecture. The architecture consists integration of homogenous and heterogeneous databases and other structured and unstructured data sources. Students will apply concepts of distributed recovery and optimization, and other related topics.Prerequisites: none
Credits: 3
Content covered will include the following: scientific process; sampling bias; hypothesis tests; confidence intervals; risk analysis vs assessment; statistical analysis concepts. Issues with qualitative and quantitative risk analysis methodologies. Exposure to and practice with multiple risk analysis methodologies, including at least one that is considered a standard.Prerequisites: none
Credits: 3
Content covered will include the following: analyze audience; define report outline and objectives for target audience (IT, executives, audit & compliance); ethos/pathos/logos concepts; white papers. Data misrepresentations, intentional or unintentional; appropriate use of data visualization tools and dashboards; representing needle in haystack data (low volume, high risk).Prerequisites: none
Credits: 3
Risk management strategies. Human factors, resistance to change. Design, development and evaluation of security controls; catalog of security controls; performance metrics. Management oversight; cost-benefit analysis, business impact analysis; policies, processes, standards. Technical, administrative, physical controls.Prerequisites: none
Credits: 3
This course will focus on research, design, and analysis of computer networks and data communications systems. The course will also entail detailed examination of modern communication standards, protocol systems and their implementation. Additional topics may include transmission technology, packet switching, routing, flow control, and protocols. Same as CS 662. Pre-req: IT 562 or 564Prerequisites: none
Credits: 1-4
Problems on an individual basis. Pre-req: consentPrerequisites: none
Credits: 3
Advanced software design, analysis, and development techniques under realistic time and budget constraints. Hands-on project management techniques. Emphasis of concepts through immersion in a team project of significant size. Same as CS 680. Pre-req: IT 580Prerequisites: none
Credits: 3
Statistical package programs used in data collection, transformation, organization, summarization, interpretation and reporting, statistical description and hypothesis testing with statistical inference. Interpreting outputs, Chi-square, correlation, regression, analysis of variance, nonparametrics, and other designs. Accessing and using large files (U.S.Census data, National Health Survey, etc.). Same as CS 690. Pre-req: a statistics coursePrerequisites: none
Credits: 1-6
A course designed to upgrade the qualifications of persons on-the-job. Pre-req: consentPrerequisites: none
Credits: 3
Student will integrate their health-related background with the practical application of scientific and professional knowledge, behavior, and skills. Students will employ health advocacy strategies, principles of quality improvement, healthcare policy knowledge, and cost-effectiveness as part of an inter-professional team to analyze data and develop a strategy to impact practice improvements in order to increase the quality and efficiency of healthcare delivery, improve satisfaction, or manage health-related costs.Prerequisites: none
Credits: 1-2
Preparation of a master's degree alternate plan paper under the direction of the student's graduate advisor. Pre-req: consentPrerequisites: none
Credits: 1-6
Preparation of a master's degree thesis under the direction of the student's graduate advisor. Pre-req: consentPrerequisites: none
