Course List

Master of Science in Health Data Analytics

The online Master of Science in Health Data Analytics is a 41 credit hour program that requires 35 credit hours in core courses, 3 credit hours of capstone course preparation for the Certified Health Data Analyst (CHDA) examination, and 3 credit hours of practicum in an ongoing real world data analytics program. To graduate, students must have an overall 3.0 GPA in coursework.

You may complete your degree as either a full-time or part-time student.


  • Click to Show/HideFull-Time Degree Path

    Students enrolled in the full-time option can complete their degree in 2 years.

    Course Title Credit Hours
    Fall I (11 hours)
    PHPH 523 Public Health in the U.S. 2
    PHST 620 Introduction to Statistical Computing 3
    PHST 661 Probability 3
    PHMS 644 Biomedical Foundations for Health Analytics 3
    Spring I (9 hours)
    PHMS 643 Data Management in Health Service Research 3
    PHST 662 Mathematical Statistics 3
    PHST 684 Categorical Data Analysis 3
    Summer I (3 hours)
    PHMS 639 Health Data Analytics Practicum 3
    Fall II (9 hours)
    PHMS 641 Data Mining I 3
    PHMS 682 Population Health Information Management 3
    PHMS 638 Data Security and Electronic Health Records 3
    Spring II (9 hours)
    PHMS 642 Data Mining II 3
    PHMS 636 Leadership in Health Information Management 3
    PHMS 637 MSHDA Capstone Course 3
    Minimum Total Credits Required 41

  • Click to Show/HidePart-Time Degree Path

    Students enrolled in the part-time option are able to complete their degree in 48 months if they take 5-6 credits per semester and plan their degree path to meet the following course sequence.

    Note: additional courses can be taken in any order, throughout the duration of the program.

    Course Title Credit Hours
    1st Semester
    PHST 620 Introduction to Statistical Computing 3
    PHST 661 Probability 3
    2nd Semester
    PHST 662 Mathematical Statistics 3
    PHST 684 Categorical Data Analysis 3
    3rd Semester
    PHMS 641 Data Mining I 3
    PHMS 638 Data Security and Electronic Health Records 3
    4th Semester
    PHMS 642 Data Mining II 3
    Additional courses – may be taken in any sequence, as scheduling allows:
    PHPH 523 Public Health in the U.S. 2
    PHMS 636 Leadership in Health Information Management 3
    PHMS 639 Health Data Analytics Practicum 3
    PHMS 643 Data Management in Health Service Research 3
    PHMS 664 Biomedical Foundations for Health Data Analytics 3
    PHMS 682 Population Health Information Management 3
    Last course taken:
    PHMS 637 MSHDA Capstone Course
    This is prep for the CHDA certification exam and should be taken close to sitting for the exam.
    3
    Minimum Total Credits Required 41


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Course Descriptions

PHMS 636 Leadership in Health Information Management

The course introduces core concepts and key issues related to healthcare provision, management and leadership, and provides a foundation of health information management for effective leadership roles in health data analytics. Students will learn how to plan, develop, and implement the governance requirements and selection process of health data analytics projects then apply these competencies into real-world problems.


PHMS 637 MSHDA Capstone Course

This course is designed to provide final preparation of the student to sit for the Certified Health Data Analyst Examination offered by the Commission on Accreditation for Health Informatics and Information Management (CAHIIM) and the American Health Information Management Association (AHIMA). The certification with the MSHDA degree provides prospective employers evidence of the student’s ability to perform professional level health data analytics. This course is also intended to provide a cumulative, rigorous, and discovery-based project.


PHMS 638 Data Security & Electronic Health Records

The course will focus on the framework, the real-world use, and the critical data security issues in deployment of Electronic Health Records (EHRs) to improve the quality of health care delivery. Students will learn functionality of EHRs through hands-on labs, technical infrastructures require for EHRs (e.g., architecture, network, security design), understand how EHRs change healthcare delivery workflows, best practice for deploying EHRs (e.g., project management, typical budgets, system selection, HIPAA governmental requirements, funding), and data security-related issues critical to EHRs implementation.


PHMS 639 Health Data Analytics Practicum

The practicum experience places the student in a non-academic environment where health data analytics are used for decision support and strategic planning. The deliverables will include (1) a written report to the instructor on the experience gained during the practicum, and (2) an outline on the activities specific to the site where the practicum is completed. The practicum should include no less than 200 contact hours at the practicum site. The manager at the practicum site will be asked to complete an evaluation of the student.


PHMS 641 Data Mining I

The course is first in a two semester sequence graduate level introduction to data mining/big data analytics. It focuses on practical implementation and interpretation of the most commonly used techniques in analysis of very large datasets.


PHMS 642 Data Mining II

This is the second of a two semester graduate level course on data mining/big data analytics. It focuses on practical implementation and interpretation of the most commonly used techniques in analysis of very large datasets.


PHMS 643 Data Management in Health Service Research

Course allows students to pursue study with faculty guidance on data management in health service research.


PHMS 644 Biomedical Foundations for Health Analytics

This course will offer an integrative molecular and biological perspective on public health problems and health data analytics. Students will explore population biology and ecological principles underlying public health and reviews molecular biology in relation to public health biology. Lectures focus on specific diseases of viral, bacterial, and environmental origin. Instructors will use specific examples of each type to develop the general principles that govern interactions among susceptible organisms and etiologic agents and devotes special attention to factors that act in reproduction and development. The course will focus on common elements including origin and dissemination of drug resistance, organization and transmission of virulence determinants, modulation of immune responses, disruption of signal transduction pathways, perturbation of gene expression, as well as the role of the genetic constitution of the host.


PHMS 682 Population Health Information Management

This course is designed to introduce students to key concepts and issues surrounding the adoption and use of information systems for population health management.


PHPH 523 Public Health in the U.S.

Course covers the history of and issues facing public health in the United States.


PHST 620 Introduction to Statistical Computing

Prerequisite: Enrolled in PH MPH and PHST 500. This course provides an introduction to SAS. It will give students an overview of the SAS system under MS Windows and provide fundamental grounding in the environment for accessing, structuring, formatting and manipulating data. Students will learn how to summarize and display data, and the inference between data steps and procedures to get information out of data.


PHST 661 Probability

Introduction to probability theory. Topics include axioms of probability, conditional probability, discrete and continuous random variables, probability distributions and joint distributions, moments, moment generating functions, mathematical expectation, transformations of random variables, limit theorems (Law of Large Numbers and Central Limit Theory).


PHST 662 Mathematical Statistics

A first course in statistical theory. Topics include limiting distributions, maximum likelihood estimation, least squares, sufficiency and completeness, confidence intervals, Bayesian estimation, Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests and asymptotic distributions.


PHST 684 Categorical Data Analysis

Focuses on statistical methods for analyzing categorical data; topics include inference for two-way contingency tables; models for binary response variables, including logistic and logit models; models for ordinal data; mutinominal response data; and analysis of repeated categorical response data. Emphasis will be placed on methods and models most useful in clinical research.



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