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Computer Science


Staff

Department Head: David Gerhard, PhD

Graduate Co-ordinator: Orland Hoeber, PhD

Faculty Listing: http://www.cs.uregina.ca/People/faculty.html


Department Description

The Department of Computer Science offers programs of study involving interdepartmental, multi-institutional and inter-institutional collaboration that has attracted faculty members and graduate students from all over the world. Students may pursue full-time or part-time graduate study leading towards the MSc and PhD degrees.

The MSc and PhD degrees in Computer Science focus on four main areas of research: artificial intelligence; databases and information retrieval; graphics, image and audio processing; multimedia, and software engineering. Specifically, active research topics conducted by faculty members include, but are not limited to:

  • Data mining, knowledge discovery and machine learning, Bayesian networks, rough set theory, uncertainty management, quantitative and temporal reasoning
  • Graphical modeling and rendering algorithms, animation, image and signal processing, facial recognition, computational music and audio, information visualization, human computer interaction
  • Information retrieval, cognitive informatics, Web intelligence and service, electronic commerce, database theory, and information theory and its applications in communications
  • Language-based software security, data security, agent-oriented software engineering, software reuse, formal methods, and distance education
  • Algorithm design and analysis, theory of computing, computational geometry, graph theory

The Department of Computer Science maintains several research laboratories: Animation Software Design, Artificial Intelligence, Graphics, Intelligent Database System, Interactive Media, Computational Discovery, Multimedia Gaming, Open Systems, Rough Computing, Rough Music and Audio Digital Interactive (aRMADILo), Saskatchewan Research Network Digital Media, Software Engineering, and Web Intelligence. Both the TR and New Media Studio laboratories result from collaborative research with various partners from industry, university, and government.

For detailed information about the research interests of faculty members and ongoing research of graduate students, please visit the Department’s website at http://www.cs.uregina.ca/.


Program Requirements and Procedures

The Department offers both a MSc and a PhD program in Computer Science. Areas of research specialization include Artificial Intelligence, Databases, Data Mining, Graphics, Human Computer Interaction, Interactive Multi-media, Software Engineering and Uncertain Reasoning.

For fully qualified students, the MSc program provides four options for completing the degree requirements: thesis, project, co-op or course only.

For the MSc, one course, at most, at the 400-level is allowed. No more than 2 directed reading or special topics classes may be used in a program. The courses taken may include at most 2 courses outside of Computer Science. Program requirements are slightly different depending on which option is chosen. MSc and PhD students are required to do two seminar presentations that are not associated with program credit hours.

The following presents the MSc program requirements for each program option. Students must choose the MSc program option they will be following at the time of application.


MSc Program

Thesis Route
The Master's thesis route requires students to pursue research supported by the Department of Computer Science. A fully qualified student may complete a Master's thesis route by undertaking 15 credits of coursework as well as 15 credits of thesis research together with the thesis defense. Two non-credit CS seminar presentations are also required.

MSc - Thesis route  (30 credit hours)

CS 8xx* 3 cr hrs
CS 8xx* 3 cr hrs
CS** 3 cr hrs
CS/non-CS** 3 cr hrs
CS/non-CS** 3 cr hrs
CS 901 15 cr hrs
CS 900 0 cr hrs
CS 900 0 cr hrs
TOTAL 30 cr hrs

* may not be a directed study or selected topics reading class
** one of these may be a 400-level class (others are 800-level)

Project Route
A fully qualified student may complete a Master's project route by undertaking 21 credits of coursework, 9 credits of professionally oriented project research, and project defense. In addition, the student is required to give two non-credit CS seminar presentations.

MSc - Project route (30 credit hours)
In the project route students must successfully complete a minimum of seven courses and a research project undertaken in the field together with a project report, presentation and defense, coupled with two non-credit seminar presentations.

CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS* 3 cr hrs
CS/non-CS* 3 cr hrs
CS/non-CS* 3 cr hrs
CS 902 or CS 901** 9 cr hrs
CS 900 0 cr hrs
CS 900 0 cr hrs
TOTAL 30 cr hrs

* one of these may be a 400-level class (others are 800-level)
**It is recommended that students register in CS 902; however, CS 901 will be accepted for those students who have transferred to the MSc project route from another MSc route (such as thesis) in Computer Science.

Co-op Route (program has been suspended effective 201820)
The Master's co-op route requires students to pursue research areas supported by the Department of Computer Science. A fully qualified student may complete a Master's co-op route by undertaking 21 credits of coursework; 12 credits of professionally oriented project research; and 3 credits of co-op education placement project report, presentation and defense.

MSc - Co-op route (36 credits) (program has been suspended effective 201820)

CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS* 3 cr hrs
CS/non-CS* 3 cr hrs
CS/non-CS* 3 cr hrs
CS 601 6 cr hrs
CS 602 6 cr hrs
CS 600   3 cr hrs
TOTAL 36 cr hrs

* one of these may be a 400-level class (others are 800-level)

Course Route

A fully-qualified student may complete a Master's course-based route by undertaking 30 credits for coursework.  In addition, the student is required to give two non-credit seminar presentations.

MSc - Course route (30 credit hours) (effective 201830)

CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 4xx/8xx* 3 cr hrs
CS 4xx/8xx* 3 cr hrs
CS/non-CS 8xx 3 cr hrs
CS/non CS 4xx/8xx* 3 cr hrs
CS 900 0 cr hrs
TOTAL 30 cr hrs

* maximum of two 400-level courses may be taken (others are 800-level)

Pre-Approved Non-Computer Science Courses
The following courses have been pre-approved and will satisfy the non-CS course requirement in all CS graduate programs listed above.  Please note that this is not meant to be an exhaustive list of the non-CS courses that may be taken.  Its only purpse is to itemize those courses that have already been examined and approved.  Students are encouraged to consider courses not on the list that are relevant to their programs, whild being reminded that all non-CS courses not on the list must be approved.  Please see the relative programs areas on the FGSR website for course descriptions.

Courses in Electronic Systems Engineering:
ENEL 489, 492, 495, 811, 812, 813, 850, 857

Courses in Software Systems Engineering:
ENSE 483, 882, 885AJ

Courses in Mathematics and Statistics:
MATH 809, 827, 869, STAT 852, 871

Courses in Busness Administration:
GBUS 866


PhD Program


After a MSc in Computer Science, the PhD program consists of at least 9 credit hours of course work and 51 credit hours of research resulting in the presentation of a substantial thesis. In addition, the student is required to give two non-credit CS seminar presentations. Successful completion of the PhD course requires a minimum of three (3) full years.

PhD - (After MSc)

CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 8xx 3 cr hrs
CS 900 0 cr hrs
CS 900   0 cr hrs
CS 901 51 cr hrs
TOTAL 60 cr hrs

*A minimum of 9 credit hours of course work are required, and course selection must adhere to the following conditions:

1) Courses must be chosen in consultation with the supervisor or co-supervisors
2) Only one course may be taught by the supervisor or co-supervisor
3) Only one course may be a directed reading

Course Descriptions

CS 600 Graduate Co-op Report (3)
The student makes a formal presentation of the report.
Note: Completion of CS 601 and CS 602 are required prior to registration in CS 600.

CS 601 Graduate Co-op Work Term I (0)
This is the first one semester graduate work experience placement for graduate students in Computer Science. To register in this class a student must be in good standing and enrolled full-time in a Master’s program in Computer Science. A preliminary work term report must be submitted before the end of the semester. A student who completes both CS 601 and CS 602 will have the designation “Co-Operative Education” added to their degree.
Prerequisites: two CS 800-level graduate courses, CGPA of at least 80%

CS 602 Graduate Co-op Work Term II (0)
This is the second one semester graduate work experience placement for graduate students in Computer Science. To register in this class a student must be in good standing and enrolled full-time in a Master’s program in Computer Science.  A final work term report must be submitted before the end of the semester. A student who completes both CS 601 and CS 602 will have the designation “Co-Operative Education” added to their degree.
Prerequisites: CS 601, CGPA of at least 80%

CS 802 Analysis and Design of Parallel Algorithms (3)
Theoretical and practical aspects of parallel algorithms; functional descriptions of various parallel models of computations; interconnection networks for multi-computers. Prior to registering in this course, students should have a background in parallel computing comparable to the senior undergraduate level.

CS 805 Computer Graphics (3)
Geometric and other advanced modelling techniques; image rendering and synthesis techniques; interactive graphics; issues in computer animation. Prior to registering in this course, students should have a background in computer graphics comparable to the senior undergraduate level.

CS 807 Interactive Hardware and Embedded Computing (3)
Hardware design for physical and pervasive computing systems.  Wired and wireless communication protocols; sensors and actuators; resource constraints; location- and context -awareness.  Applications include wearable computing, wireless sensor networks, robotics and automation, internet of things.  Embedded hardware platforms such as ARM (raspberry Pi) and AVR (Arduino).

CS 808 Advanced Animation Software Design (3)
Principles of animation. Current research areas in animation software design. Features and architecture of animation software. Timelines, motion pathways, parametric key framing kinematics, gaseous phenomena, and facial animation.

CS 809 Interactive Entertainment Software (3)
This course surveys current research on the design and implementation of interactive entertainment software, including computer games. Topics include: interactivity, principles of interactive entertainment, hardware platforms, current software development tools and languages, game loop, design of virtual worlds and virtual characters, real-time requirements, incorporating multimedia resources, aesthetics.

CS 811 Theory of Computing (3)
Study of fundamental concepts of computer science from the theoretical point of view; basic concepts of computational complexity theory, algorithm analysis and their relation to the set of problems which can be programmed; "good" algorithm design. Prior to registering in this course, students should have a background in introductory compiler design, or algorithm analysis comparable to the senior undergraduate level.

CS 815 Computer Vision (3)
Sensing techniques; sensing data pre-processing; higher level scene descriptions; model-based recognition; motion analysis. Prior to registering in this course, students should have a background in image processing comparable to the senior undergraduate level.

CS 820 Artificial Intelligence (3)
Logics; natural language processing; knowledge representation; uncertainty reasoning; machine learning; expert systems; neural networks. Prior to registering in this course, students should have a background in artificial intelligence comparable to the senior undergraduate level.

CS 824 Information Retrieval (3)
Content analysis; types of storage and retrieval systems; retrieval models; information theory; multimedia retrieval; hypertext; information network and inference. Prior to registering in this course, students should have a background in algorithms and data structures, and database and information retrieval comparable to the senior undergraduate level.

CS 825 Image Processing (3)
Image models; image transformations; enhancement and restoration techniques; image segmentation; feature extractions and higher level descriptions. Prior to registering in this course, students should have a background in image processing, and numerical and symbolic computing comparable to the senior undergraduate level.

CS 826 Bioinformatics and Biomedical Applications (3)
This course provides an introduction to research in bioinformatics, which is the analysis of biological and medical data. Topics include sequence and image analysis, modelling of complex processes, biomedical database organization, and biomedical data mining. Selected biomedical data applications are also examined.

CS 827 (327) Computer Audio (3)
Representation of audio, audio compression, spatialization and surround sound. Analysis and synthesis of sound waveforms. Speech and music. Temporal and spectral processing.

CS 828 (305) Human Computer Communication (3)
Theory and practice related to the design and implementation of usable software and easy-to-learn interfaces. Specific topics will include user-centered design and task analysis; prototyping and the iterative design cycle; interface design and methods of evaluation.

CS 829 Information Theory and Applications (3)
This course covers the fundamentals of information theory and its application in content distribution over the Internet. Topics covered include: information theory, channel codes, content distribution network, and peer-to-peer network coding. Prior to registering in this course, students should have a background in Data Communications and Networks comparable to the senior undergraduate level.

CS 830 Machine Learning (3)
Models of learning; inductive inference; constructive and selective induction; learning from examples; explanation-based learning; machine discovery; grammatical inference; knowledge acquisition; applications. Prior to registering in this course, students should have a background in artificial intelligence comparable to the senior undergraduate level.

CS 831 Knowledge Discovery in Databases (3)
Knowledge discovery from databases is the nontrivial extraction of implicit, previously unknown, and potentially useful information from databases. This course focuses on data sources, extraction techniques, efficiency concerns, and measures of novelty and usefulness. Prior to registering in this course, students should have a background in database and information retrieval, and artificial intelligence comparable to the senior undergraduate level.

CS 833 Operating Systems (3)
Multiple processes and scheduling; resource management; storage management; file systems; deadlock problem; queuing models; distributed systems; fault tolerant systems; operating systems for parallel architectures. Prior to registering in this course, students should have a background in an introduction to operating systems, and computer system architecture comparable to the senior undergraduate level.

CS 834 Fundamentals of Computer Systems Security (3) effective 201920
This course presents the objectives and the fundamentals of computer and network system security: confidentiality, integrity, availability, authentication, and authorization. Common security concepts are detailed, such as cryptography, symmetric/asymmetric encryption, digital signature, certificate authority, hashing, communication protocol security, and audit. Mathematical foundations and applications of these methods will be explained.

CS 835 Pattern Recognition (3)
Statistical pattern recognition; parameter estimation and supervised learning; nonparametric techniques; linear discriminant functions; unsupervised learning and clustering; syntactic pattern recognition; applications. Prior to registering in this course, students should have a background in algorithms and data structures comparable to the senior undergraduate level.

CS 836 Rough Sets and Applications (3)
Theory of rough sets is a fundamental mathematical methodology for modelling classification or decision problems involving imprecise or uncertain information. Its implications include pattern classification, data mining, machine learning, control algorithm acquisition from data, circuit design and others. The course will provide the basics of the methodology and will include the study of the above applications of rough sets. Prior to registering in this course, students should have a background in discrete computational structures, artificial intelligence and statistical methods comparable to the senior undergraduate level.

CS 837 Information Visualization (3)
Information Visualization focuses on the desgin, development, and study of interactive visualization techniques for the analysis, comprehension, exploration, and explanation of large collections of abstract information.  Topics to be covered include principles of visual perception, information data types, visual encodings of data, representations of relationships, interation methods, and evaluation techniques.

CS 838 Uncertain Reasoning in AI (3)
Advances in using uncertain knowledge to make decisions rationally and effectively (for diagnosis, trouble shooting, robot navigation, etc.). Focus on probabilistic approach and graphical modeling to aid inference. Topics include criteria for uncertainty management, comparison of schemes, Bayesian/Markov networks, influence diagrams, chain graphs, inference algorithms, elicitation and learning of belief networks. Prior to registering in this course, students should have a background in discrete computational structures, artificial intelligence and statistical methods comparable to the senior undergraduate level.

CS 839 Web Intelligence and Electronic Commerce (3)
The course investigates research topics related to Web Intelligence and Electronic Commerce. The topics include: web technology, network infrastructure, web-based business models, agents, Extended Markup Language, web mining, security, web information filtering and retrieval, and intelligent information systems.

CS 842 - Introduction to Data Science Fundamentals (3)
Introduction to Data Science provides a broad overview of the data science process lifecycle which includes data discovery, data preparation, model planning, machine learning model implementation and evaluation, visualization, and delivery. The course provides hands-on data science experience via a real-world project.

CS 855 Mobile Computing (3)
Mobile Computing focuses on conducting research in the design and development, and evaluation of software in a networked mobile environment.  The primary topics to be covered in the course include network computing, graphics programming, human-computer interaction, and evaluation methods, all focused on the challenges and opportunities afforded by modern mobile computing devices.

CS 858 – Virtual and Augmented Reality (3) effective 202010
Design and implementation of software in virtual and augmented reality environments. Development practices, assets and avatars, interaction, locomotion, psychological effects, audio, multiplayer considerations, applications. Limitations and future developments.

CS 872 Software Engineering (3)
Review of fundamental concepts; project planning; requirements analysis; program design, implementation and testing; object-oriented development; metrics and cost estimation; software reuse; CASE technology; configuration management; software engineering and Ada. Prior to registering in this course, students should have a background in software engineering methodology comparable to the senior undergraduate level.

CS 875 Database Systems (3)
Database management system architecture; relational, network and hierarchical data models; theoretical and practical aspects of database applications; study of data definition and data manipulation facilities and database management systems; security and integrity; distributed database management system architecture. Prior to registering in this course, students should have a background in advanced topics in database systems, and database and information retrieval comparable to the senior undergraduate level.

CS 890AA-ZZ Directed Readings (3)
Readings in programming languages; artificial intelligence; numerical computation; database management systems; graphics and computer vision; architecture; software engineering.

CS 900 Computer Science Graduate Seminar (0)
CS Graduate students must complete two semesters of CS 900, with the exception of MSc Course Route students, who must complete one semester of CS 900. When enrolled in CS 900, a student must make one presentation and attend all presentations. In the first semester of CS 900, the student will choose a Computer Science topic within their research area. In the second semester of CS 900 (if required), the student will choose a topic within their own research.

CS 901 Research (Variable credit 3-15)
Thesis research.

CS 902 Computer Science Project Research (Variable credit 3-6)
A supervisor approved project requiring an in-depth student investigation of a CS problem.

 


Masters of Health Information Management (MHIM) Program
(this program is inactive effective immediately)


The Masters of Health Information Management program is an online masters program aimed at health and information technology professionals in the health sector. It is anticipated that the majority of the students will study on a part-time basis while they continue to work full-time. The online format of the program will allow students to study at the time and place that is most convenient for them. There are two options available to complete the program:

1. A Course Route consisting of 30 credit hours (10 courses)
2. A Project Route consisting of 24 credit hours (8 courses) plus 6 credits of project hours

The Course Route is mandatory for students who want to qualify for eligibility to challenge the Canadian College of Health Information Management’s (CCHIM) National Certification Examination (NCE).

The Project Route is an option for students who are already certified CHIM professionals. An Applied Project is a combination of course work and work-based learning. This component occurs in the latter stage of the program.

Admission Requirements:

  • A baccalaureae degree from an accredited university or college with a minimum Grade Point Average (GPA) of 75%.
  • Official transcript indicating at least:
    - One course (3 cr hrs) in Statistics
    - One course (3 cr hrs) in English (Please note this requirement will be removed effective 202030)
    - One course (3 cr hrs) in Science
    - Two courses (6 cr hrs) in Computer Science or other courses that prove the applicant's computer proficiency.  (CS 100 and CS 110 equivalent)
    - The final grade for each of the courses mentioned above must be at least 75%.
  • Curriculum Vitae verifying completion of at least three years of professional work experience preferably in a health care sector.
  • Two letter of recommendation.  One letter should come from a professional who has supervised the candidate's work.  The other letter should be written by someone who is well situated to comment on the candidate's academic work.  Both reference letters should address the candidate's interpersonal, academic and professional abilites. 
  • An essay of no less than 500 words describing the student's motivation to pursue a graduate degree in Health Information Management, the skills that will help the student complete the program, and the student's career goals connected to this degree. 

    International Student Eligibility
    International students do not need a study permit to engage in an online program offered by a Canadian institution, nor do they need a study permit to be issued the degree upon completion of their program of study. However, international students enrolled in the MHIM program will need to obtain a study permit in order to attend a one week face-to-face seminar included in the HIM 804 Leadership in Health Organizations course.

MHIM Course Route

MHIM 800 3 cr hrs
MHIM 801 3 cr hrs
MHIM 802 3 cr hrs
MHIM 803 3 cr hrs
MHIM 804 3 cr hrs
MHIM 805 3 cr hrs
MHIM 806 3 cr hrs
MHIM 807 3 cr hrs
MHIM 808 3 cr hrs
One of:
  MHIM 809
  MHIM 810
  MHIM 811
3 cr hrs
TOTAL 30 cr hrs


MHIM Project Route

MHIM 800 3 cr hrs
MHIM 801 3 cr hrs
MHIM 802 3 cr hrs
MHIM 804 3 cr hrs
MHIM 900 3 cr hrs
MHIM 902 3 cr hrs
Four of:
  MHIM 803
  MHIM 805
  MHIM 806
  MHIM 807
  MHIM 808
  MHIM 809
  MHIM 810
  MHIM 811
12 cr hrs
TOTAL 30 cr hrs

Course Descriptions

MHIM 800 - Statistics and Research Methods (3)
The objectives of this course are to provide an understanding of both quantitative and qualitative research methodologies, as well as an introduction to statistical concepts, methods and applications useful for health care and HIM professionals. The emphasis will be on application of statistical tools to support clinical and managerial decision making and identifying statistical tests and methods appropriate for the data and research design. Use of a computer-based statistical package will be required.

MHIM 801 - Data Management (3)
The objectives of this course are to understand data governance, data quality, data management, data standards, and data integrity. Students will learn the principles of data governance and develop policies and procedures to support the HIM life cycle. Data quality frameworks will be examined. The issues around data management and data integrity will be explored. Canadian health care databases will be used to create presentations.

MHIM 802 - Health Information Management (3)
The objectives of this course are to introduce principles of information governance, information management (HIM life cycle), and information storage and retrieval systems. Students will expand upon the topics learned in HIM801 Data Management to include the organizational structure of health care institutions, health information departments, disease registries, the medical record, and professional associations of the health information manager and tumor registrar. Various aspects of information storage and retrieval systems, documentation requirements, and public health and hospital statistics will be studied.

MHIM 803 - Coding and Classification (3)
The objectives of this course are to learn the concepts of coding classification and data capture to support health care policy, and to introduce the classification and terminology systems used in Canada and internationally. Students will be introduced to the principles of taxonomy and the purposes of classification systems. Students will interpret and apply terminologies, vocabularies, nomenclatures, and classification systems. Mapping of clinical vocabularies and terminologies to appropriate classification systems will be discussed. This course includes a lab component.

MHIM 804 - Leadership in Health Organizations (3)
This course focuses on leadership in a health organizational context and preparing students to undertake leadership roles in their HIM careers. Topics include leadership models and theories, critical thinking, change management, workflow analysis, human resource management, strategic planning, financial management, and ethics. It includes a one week face-to-face seminar.

MHIM 805 - Introduction to Health Informatics and Information Technology (3)
This course is an integrative study of the Information Technology (IT) used in all facets of health care administration and delivery. Emphasis is on the management, synthesis, and transformation of information for tactical and strategic decision making throughout the health care enterprise. Understanding of the underlying principles of networks, data storage, and the capabilities of modern computer architecture and software will be covered. Topics include e-health and electronic medical records, IT deployment and adoption, data security and data interoperability, privacy, confidentiality, information management planning, and legal and ethical issues related to IT and their implications on practice for the health care administrator and HIM professional. This course will address the theoretical and pragmatic issues related to Electronic Health Record (EHR) technology.

MHIM 806 - Health Information Systems (3)
This course is a continuation of HIM805 Introduction to Health Informatics and Health Information Technology. The course will build upon the basic concepts of the analysis, design, implementation, and evaluation of health information systems learned in the pre-requisite course. The course includes an introduction to the basic concepts of the analysis, design, implementation, and evaluation of health information systems. Students will be provided the opportunity to develop skills and ability in defining information needs, interpreting the capabilities of health information systems, setting forth the feasible alternatives, and guiding the appropriate diffusion of information science technology into the health care system.

MHIM 807 - Analytics and Decision Support (3)
Upon completion of this course, students will be able to: explain the health system use of data (primary and secondary uses of health data); apply meaningful use of data; participate in clinical and administrative decision support; design data sources for intelligence extraction; and create business intelligence through data analytics; and create data visualization techniques. A laboratory component will be incorporated.

MHIM 808 - Health Databases and Database Management (3)
The objective of this course is to provide students with the ability to design and implement a relational database. This course addresses database theory, methodologies for database design, and issues related to database administrations specifically in a healthcare context. Emphasis is on requirements and methodologies for assuring data integrity and security in healthcare enterprise information systems, specifically in relationship to the database environment. An introduction to relational databases and the fundamental concepts is necessary for the design, use, and implementation of relational database systems.

MHIM 809 - Health Information Privacy (3)
In this course, students will design a privacy and security infrastructure including policies and procedures involving data collection, use, access, disclosure, retention, storage, destruction of paper, hybrid, electronic, digital images, voice recording and electronic mails. Privacy, security, and confidentiality policies and procedures will be discussed and developed. The legislative and regulatory requirements surrounding the release of health information at the individual and aggregate levels will be explored. Students will learn to educate staff and clinicians on health information protection methods. Risk assessment techniques will be discussed with a focus on access and disclosure management.

MHIM 810 - Finance and Compliance Management (3)
This course introduces students to an in-depth analysis of financial planning and management, risk and investment policies relating to HIM. The course serves as a framework for understanding a broad range of corporate financial decisions. This course addresses a growing need to adequately train health care leaders in the field of health care compliance and various topics in health care compliance, including corporate compliance (fraud and abuse, accountability reporting requirements, activity based funding), privacy, risk management and identity theft. Essential elements of a corporate compliance program will be presented. Privacy of personal health information will be discussed in terms of relevant legislation.

MHIM 811 - Health Information Projects Management (3)
The objectives of this course are to provide students with the necessary tools to assess project management tools, design strategic planning research models and methods, engage stakeholders in information governance initiatives, as well as propose innovative healthcare policies. Students will learn how to use research methods to integrate best practices in project planning and management.

MHIM 900 - Applied Project Proposal and Research (3)
Individual study hours under the supervision of a faculty member. Students will prepare a proposal for an applied project and conduct research supporting a topic related to health information management.

MHIM 902 - Applied Project Development and Presentation (3)
Students will conclude research supporting a topic related to health information management resulting in the writing of a comprehensive paper and public presentation of an applied project.