Steps to Apply

1. Completed Graduate Application
2. Submit $65 non-refundable application fee
3. Submit GRE Quantitative section score
4. Submit current curriculum vitae (CV)
5. Submit statement of goals (i.e., general research interests)
6. Submit official transcripts of all undergraduate and graduate work from regionally-accredited institutions – Have transcripts sent directly to UofL.

Admission Requirements

The M.S. in Health Data Analytics is available to students who have completed an undergraduate degree in biostatistics, statistics, mathematics, computer science or a related discipline. Applicants must also have competency in college-level calculus, statistics and regression analysis, as evidenced by transcripts from postsecondary institutions.

• Bachelor’s degree
  • Must be in biostatistics, statistics, mathematics, computer science or a related discipline
  • Competency in college-level calculus, statistics, regression analysis
• 2 letters of recommendation written within past 12 months
• Successful admission interviews with the HMSS Health Leadership Committee

International Students – Additional Notes/Requirements

The Health Data Analytics master’s program is open to international students. Additional requirements apply.

1. All transcripts not in English must be certified as authentic and translated verbatim in English.
2. Students whose primary language is not English must show English language proficiency by attaining one of the following:
   • Total score of 80 or higher on the Test of English as a Foreign Language (TOEFL) Internet-based test or 6.5 or higher on the International English Language Testing System (IELTS), OR
   • Successful completion of the exit examination for the advanced level of the Intensive English as a Second Language Program (IESL) at the University of Louisville, OR
   • Demonstration of a degree award from an acceptable English language institution
3. International students will not be issued a U.S. visa if admitted to an online program, since there are no campus attendance requirements.

For more information on the admission and application process, please contact our Online Learning Enrollment Counselors at 800.871.8635 or by email at online@louisville.edu.

Start Your Application

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 a real world data analytics program at an organization of choice. To graduate, students must have an overall 3.0 GPA in coursework.

• Full-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

  
  
• Part-Time Degree Path
  
  

  Part-time students must be cognizant that courses are offered on an alternating basis, usually every two years. Thus, part-time students must pursue the recommended course sequence, as courses are available.

  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

  
  

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.

• What GPA do I need to enter the program?

  There is no required GPA for admission into the Master of Science in Health Data Analytics program. However, students in the program will need to maintain a 3.0 to graduate. Grades in previously taken courses relevant to the degree (math, statistics, etc.) will be a strong indication of student success in this program.

• What GRE score do I need to be accepted into the program?

  While there’s no minimum GRE to apply to the program, a strong quantitative background is recommended. Therefore, successful students will typically score above the 40th percentile in the quantitative reasoning section of the test.

• I want to enroll part-time. How long will it take me to complete the program?

  Since certain courses are only offered in the Fall or Spring semesters (see Course List [hyperlink] for details), part-time program students will take around 4.5 years to graduate.

• What is the Certified Health Data Analyst (CHDA) Examination?

  In addition to receiving your master’s degree, the CHDA certification from the American Health Information Management Association boosts your professional credentials and reputation as a health data expert. Completing your master’s program with UofL will effectively prepare you to sit for this exam, but it is by no means a requirement for graduating from the program.

• Where and when will I complete the Practicum component of the program?

  The practicum is an exciting component of the program that allows you to get hands-on learning experience in the health data analytics field. The practicum will be completed at a location of your choice, within your local community (note: you can complete your practicum with your current employer, but the work completed cannot be part of your day-to-day job tasks). Full-time students will complete the practicum during the summer between their first and second year in the program (part-time students should speak with an enrollment counselor for more information).

• How does this program differ from the online Master of Science in Biostatistics program also offered from the College of Public Health?

  Health Data Analytics and Biostatistics are both viable degree options for a career in healthcare data information analysis. At their core, both programs deal with analysis and interpretation of healthcare data with innovative tools like machine learning, artificial intelligence and analytics.

  The differences are in how the data is manipulated, structured, and analyzed. Biostatisticians will typically work with clean data sets, looking for answers to specific, targeted questions related directly to the research project.

  Data Analysts take a broader look and are typically responsible for data cleansing and preparation. Data analysts analyze information to explore connections and insights that might not yet be identified. In other words, Biostatistics is a more theory-based, whereas Health Data Analytics includes ‘data wrangling’ and looks at population level health and outcomes.

• Can I transfer graduate-level credits from another university?

  You can transfer up to six credit hours from another regionally accredited university.

  However, to be accepted, the content in the courses you have completed must closely match the required courses for the master’s in Health Data Analytics at UofL.

  The program will consider your transfer coursework when you submit your application.