Master of Science in Health Data Science and Informatics
The goal of the M.S. in Health Data Science and Informatics program is to prepare students to work as a health data scientist or health informaticist in academia or health care industries. Upon completion of the M.S. in Health Data Science and Informatics the students will have an extensive understanding of biostatistical principles and computing and informatics skills with applications in clinical and other diverse data resources.
With the rising emphasis on highly personalized data in health care, pharmaceutical, insurance and other organizations, so rises the need for health data scientists and health informaticists. The U.S. Bureau of Labor Statistics estimates a 31 percent growth in jobs for data scientists over the next 10 years (2019-2029) and reports a current median salary of $124,100. Our program will bring statistics, data science, and informatics together with a focus on health data. This combination of skill sets is highly sought after and is required in many healthcare institutions and industries.
The Master of Science in Health Data Science and Informatics degree typically complements prior education and careers in:
- Pharmaceutical Industry
- Health Care
- Insurance Company
- Consulting
- Education
- Health Analytics
- Health Research
- Government
- Biotechnology
The application for admission to the M.S. in Health Data Science and Informatics is an online process. Detailed instructions on how to apply are posted on the Department of Biostatistics & Data Science website.
ADMISSION REQUIREMENTS:
- A bachelor's degree from a regionally accredited institution documented by submission of an official transcript indicating the degree has been conferred before entering the program. Official transcripts from institutions attended post-baccalaureate are also required.
- Students with degrees from outside the U.S. may be subject to transcript evaluation indicating the degree is equivalent to a U.S. degree and meets the minimum cumulative GPA requirements.
- A cumulative grade-point average (GPA) of at least a 3.0 on a 4.0 scale for the bachelor's degree.
- Applicants who are not native speakers of English, whether domestic or international, must demonstrate that they meet the Minimum English Proficiency Requirement.
- A background check is required during the admission process; it may affect the student's eligibility to enter the program.
- Letter grade of B or better in Calculus I and II (or the equivalent) or completion of STAT 655: Foundations of Mathematics for Data Science with a grade of B or higher.
- Successful completion of a course in any computer programming language or demonstration of mastery via credentials or work experience.
- Contact information for three references who are familiar with the applicant's work and character and who have agreed to write letters of recommendation.
- A current resume or curriculum vitae.
- A personal statement describing your career goals and interest in the program.
- Graduate Record Examination (GRE) scores (or other graduate examination scores, such as the GMAT) are recommended, but not required.
Applicants will be assessed based on these requirements.
After an applicant has been admitted, a program may defer an applicant's admission for up to one year after which time the applicant must submit a new application.
Admission requirements are subject to change. In most cases, use the catalog of the year the student entered the program. Other years’ catalogs».
The program consists of 36 credit hours including annual evaluations and the successful completion of the Master’s Comprehensive Examination.
The M.S. in Health Data Science and Informatics degree program consists of 36-credit program. Students choose one or more emphasis area(s) from Health Data Science and Health Informatics. The program is organized into three sections: 20 credit hours of required statistics and computing foundation, 10 credit hours of specific to the emphasis area, and 6 credit hours of elective courses designed to equip students with skills in statistical and computational methods for the acquisition and analysis of Big Data.
DEGREE REQUIREMENTS:
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Degree requirements are normally completed within 2 years of admission to the program although a maximum of 7 years is allowed.
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Completion of a minimum of 36 credit hours.
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Cumulative grade-point average (GPA) of at least a 3.0 for all KU graduate coursework.
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Enrollment in a minimum of one credit hour the semester the student will graduate.
Successful completion of a general examination the semester the student will graduate. The Health Data Science emphasis general examination is given upon completion of the following courses: HDSC 835, HDSC 840, DATA 881, and HDSC 861. The Health Informatics emphasis general examination is given upon completion of the following courses: HDSC 835, HDSC 840, DATA 881, and HDSC 831. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should be awarded the MS degree. The examination is created and administered by a committee of at least three members of the Department Graduate Faculty. If this examination is failed, a second examination may be taken no sooner than three months later and is subject to committee approval. The committee can recommend that the student leave the program following the semester in which the examination is taken. After two failures, no further examination is permitted, and the student will not be awarded the MS degree.
Required Statistics and Computing Foundation Courses (20 semester credit hours)
| Code | Title | Hours |
|---|---|---|
| HDSC 805 | Professionalism, Ethics and Leadership in the Statistical Sciences | 3 |
| HDSC 812 | Clinical Data Management | 3 |
| DATA 817 | Introduction to Tableau | 1 |
| DATA 819 | Introduction to Python | 1 |
| HDSC 823 | Introduction to Programming and Applied Statistics in R | 3 |
| HDSC 835 | Categorical Data Analysis | 3 |
| HDSC 840 | Linear Regression | 3 |
| DATA 881 | Statistical Learning I | 3 |
Health Data Science Foundation Courses (10 semester credit hours)
| Code | Title | Hours |
|---|---|---|
| DATA 822 | Introduction to SQL | 1 |
| DATA 824 | Data Visualization and Acquisition | 3 |
| HDSC 845 | Survival Analysis | 3 |
| HDSC 861 | Observational Health Data Analysis | 3 |
Health Informatics Foundation Courses (10 semester credit hours)
| Code | Title | Hours |
|---|---|---|
| HDSC 790 | Introduction to Artificial Intelligence and Machine Learning | 1 |
| HDSC 826 | Data Literacy | 3 |
| HDSC 831 | Advanced Health Informatics | 3 |
| HDSC 883 | Processing and Analysis of Medical Information Systems | 3 |
Electives (6 semester credit hours)
Successful completion of a minimum of 6 credit hours of elective coursework from the list below, or other courses under BIOS/STAT/DATA prefix offered by the department. Specific courses are determined in consultation with the student's advisor.
| Code | Title | Hours |
|---|---|---|
| HDSC 790 | Introduction to Artificial Intelligence and Machine Learning | 1 |
| HDSC 815 | Introduction to Bioinformatics | 3 |
| HDSC 820 | SAS Programming I | 3 |
| DATA 824 | Data Visualization and Acquisition | 3 |
| HDSC 826 | Data Literacy | 3 |
| HDSC 830 | Experimental Design | 3 |
| HDSC 831 | Advanced Health Informatics (Advanced Health Informatics) | 3 |
| HDSC 845 | Survival Analysis | 3 |
| HDSC 855 | Statistical Methods in Genomics Research | 3 |
| HDSC 861 | Observational Health Data Analysis | 3 |
| DATA 880 | Data Mining and Analytics | 3 |
| DATA 882 | Statistical Learning II | 3 |
| HDSC 883 | Processing and Analysis of Medical Information Systems | 3 |
- Regular attendance at the Department Journal Club and Seminar Series is required.
Degree requirements and course descriptions are subject to change. Any courses taken as an equivalent must be approved by the Graduate Director and the Office of Graduate Studies. In most cases, use the catalog of the year student entered the program. Other years’ catalogs».
ANNUAL EVALUATIONS:
Students are evaluated each May by their graduate advisors and the director of the graduate program. These evaluations provide feedback to the student regarding the progress they are making in meeting program requirements, classroom performance, and research performance.
TYPICAL PLAN OF STUDY
Students may choose to pursue one of two emphasis areas for the MS in Health Data Science and Informatics degree. The emphasis areas were developed based on the knowledge and skillset demanded by the workforce.
- Health Data Science Emphasis will provide students with skills in statistical and computational methods for visualization, analysis, and interpretation the data.
- Health Informatics Emphasis will offer students to acquire focused knowledge in statistics and computing with an application to electronic health records (EHR) data acquisition, storage, and management.
Below is the typical plan of study based on which emphasis is chosen.
HEALTH DATA SCIENCE EMPHASIS
| Year 1 | |||||
|---|---|---|---|---|---|
| Summer | Hours | Fall | Hours | Spring | Hours |
| HDSC 823 | 3 | HDSC 835 | 3 | HDSC 812 | 3 |
| HDSC 840 | 3 | HDSC 845 | 3 | ||
| DATA 824 | 3 | DATA 817 | 1 | ||
| 3 | 9 | 7 | |||
| Year 2 | |||||
| Summer | Hours | Fall | Hours | Spring | Hours |
| HDSC 805 | 3 | DATA 881 | 3 | HDSC 861 | 3 |
| DATA 819 | 1 | DATA 822 | 1 | Elective | 3 |
| Elective | 3 | General Exam should be scheduled if approved by advisor to proceed. | |||
| 4 | 7 | 6 | |||
| Total Hours 36 | |||||
HEALTH INFORMATICS EMPHASIS
| Year 1 | |||||
|---|---|---|---|---|---|
| Summer | Hours | Fall | Hours | Spring | Hours |
| HDSC 823 | 3 | HDSC 835 | 3 | HDSC 831 | 3 |
| DATA 819 | 1 | HDSC 840 | 3 | HDSC 812 | 3 |
| DATA 881 | 3 | DATA 817 | 1 | ||
| 4 | 9 | 7 | |||
| Year 2 | |||||
| Summer | Hours | Fall | Hours | Spring | Hours |
| HDSC 805 | 3 | HDSC 883 | 3 | HDSC 826 | 3 |
| HDSC 790 | 1 | Elective | 3 | Elective | 3 |
| General Exam should be scheduled if approved by advisor to proceed. | |||||
| 4 | 6 | 6 | |||
| Total Hours 36 | |||||
Dual M.D. - M.s. in health data Science and informatics (HDSCI) Program
This is a five-year program in which the medical student completes the requirements for the M.S. HDSCI degree in one year. The MS in Health Data Science and Informatics curriculum integrates statistical and data science methodologies within the context of health outcomes data, preparing students for leadership roles in health care organizations. As health care continues to evolve with advancements in biomolecular data analysis and artificial intelligence, data science skills are increasingly essential for those entering medical practice or health care research. Graduates of this dual degree program will be well-equipped for careers that require both medical and quantitative expertise. Students must complete all degree requirements for both the M.D. and the M.S. HDSCI programs. The MD program is described in the School of Medicine Catalog.
Students must be admitted to each program to pursue the dual degree. Please contact the Department of Biostatistics and Data Science Education Team for more information.
Program requirements for the Dual M.D. - M.s. in health data Science and informatics (HDSCI) Program
Students in the MD/MS Program complete requirements for the MD degree, as specified by the School of Medicine. Current KUMC medical students have two options for completing the MS portion of the joint degree: (a) in the year between years two and three of the medical school curriculum, or (b) in a year between years three and four of the medical school curriculum. Students applying to medical school may start the program the year prior to the start of medical school and complete the remaining MS courses during subsequent years of the medical school curriculum, should they be accepted. If they are not accepted in MD program, they have to complete regular (36 credit hours) MS in Health Data Science and Informatics program. The dual degree program allows current KUMC medical students to achieve both degrees in five years. The curriculum of the MD/MS in Health Data Science and Informatics is built upon required statistics and computing foundation course and required health data science or informatics foundation courses. Students may select to emphasize in either Health Data Science or Health Informatics.
At KUMC, the standard requirement for a master's degree is typically 30 credit hours. However, for students who are exceptionally well-prepared, this requirement may be reduced to as few as 24 credit hours by waiver of HDSC 805, HDSC 823, and elective credits. This reduction is contingent upon the approval of the Program Director and KUMC Graduate Studies. Therefore, it is possible to complete this MS degree program with 24 credit hours and in one year.
Prerequisite (Optional). STAT 655.
For applicants with a limited mathematics background or those who have completed their required calculus course long ago and need a refresher, enrollment in STAT 655 is recommended. This course provides essential mathematical and statistical concepts necessary for success in the MS in Health Data Science and Informatics program, ensuring students are well-prepared for the quantitative coursework.
Required Statistics and Computing Foundation Courses (at least 14 SCH)
| Code | Title | Hours |
|---|---|---|
| HDSC 805 | Professionalism, Ethics and Leadership in the Statistical Sciences (Waived based on core competency requirements of the MD program.) | 3 |
| HDSC 823 | Introduction to Programming and Applied Statistics in R (Waived for students who successfully complete SER week course Reproducible Research and Statistical Analysis in RStudio or for students who have successfully completed an undergraduate programming courses with a letter grade A.) | 3 |
| HDSC 812 | Clinical Data Management | 3 |
| HDSC 835 | Categorical Data Analysis | 3 |
| HDSC 840 | Linear Regression | 3 |
| DATA 817 | Introduction to Tableau | 1 |
| DATA 819 | Introduction to Python | 1 |
| DATA 881 | Statistical Learning I | 3 |
Health Data Science Foundation Courses (10 SCH)
| Code | Title | Hours |
|---|---|---|
| HDSC 845 | Survival Analysis | 3 |
| HDSC 861 | Observational Health Data Analysis | 3 |
| DATA 822 | Introduction to SQL | 1 |
| DATA 824 | Data Visualization and Acquisition | 3 |
Health Informatics Foundation Courses (10 SCH)
| Code | Title | Hours |
|---|---|---|
| HDSC 790 | Introduction to Artificial Intelligence and Machine Learning | 1 |
| HDSC 826 | Data Literacy | 3 |
| HDSC 831 | Advanced Health Informatics | 3 |
| HDSC 883 | Processing and Analysis of Medical Information Systems | 3 |
Electives
May be waived for students who have taken prior relevant coursework with letter grade of A and have achieved at least a cGPA of 3.5 in courses taken through this program.
| Code | Title | Hours |
|---|---|---|
| HDSC 790 | Introduction to Artificial Intelligence and Machine Learning | 1 |
| HDSC 815 | Introduction to Bioinformatics | 3 |
| HDSC 820 | SAS Programming I | 3 |
| HDSC 826 | Data Literacy | 3 |
| HDSC 830 | Experimental Design | 3 |
| HDSC 831 | Advanced Health Informatics | 3 |
| HDSC 845 | Survival Analysis | 3 |
| HDSC 855 | Statistical Methods in Genomics Research | 3 |
| HDSC 861 | Observational Health Data Analysis | 3 |
| HDSC 883 | Processing and Analysis of Medical Information Systems | 3 |
| DATA 824 | Data Visualization and Acquisition | 3 |
| DATA 882 | Statistical Learning II | 3 |
Total Credit hours = 24 SCH, SCH = Semester Credit Hours
Comprehensive Exam Requirement: Successful completion of a Master's Comprehensive Examination. The Health Data Science emphasis general examination is given upon completion of the following courses: HDSC 835, HDSC 840, DATA 881, and HDSC 861. The Health Informatics emphasis general examination is given upon completion of the following courses: HDSC 835, HDSC 840, DATA 881, and HDSC 831.
Graduates of the Health Data Science and Informatics M.S. program will be able to:
- Demonstrate an understanding of statistical analysis methods and health data informatics practices.
- Function as a collaborator on a project team.
- Demonstrate proficiency in industry-standard statistical software.
- Assume responsibility for the design and implementation of analyses for projects within health science studies.
- Assist with the design and implementation of data management systems for projects related to health science studies.
- Demonstrate a broad knowledge and understanding of diverse resources of electronic health records data and other types of data, computation approaches and visualizations.
- Prepare reports and publications resulting from the analyses for projects.
- Effectively communicate the principles of statistics, analytics, and informatics with his or her peers with varying statistical backgrounds.
- Serve as an advocate for good statistical design and practice.
Because the M.S. in Health Data Science and Informatics degree signifies that the holder is prepared for entry into the practice of data science and informatics research, it follows that graduates must have the knowledge and skills necessary to function in a broad range of academic and research situations. The Technical Standards include those physical, cognitive, and behavioral standards that are required for the satisfactory completion of all aspects of the curriculum and the development of professional attributes required by all students at graduation. Therefore, the following abilities and expectations must be met by all students with or without accommodations admitted to the M.S. program:
1. Observation. A student must be able to observe and evaluate class demonstrations and field experiences relevant to the field of data science and informatics. He or she must be able to read and comprehend text, numbers, tables and graphs, both in print and displayed electronically. Observation necessitates the functional use of the senses of vision and hearing.
2. Communication. A student must be able to communicate effectively and efficiently in English in oral, written, and electronic form with other students, faculty, staff, researchers, and the public. Effective communication includes: the ability to understand assigned readings, lectures, and technical and professional materials; the ability to analyze information; the ability to present results of such analyses verbally and in writing; the ability to independently prepare papers and presentations; and the ability to follow verbal and written instructions. Use of computers and other technology is imperative to this communication.
3. Motor. A student must have sufficient motor function to attend classes, prepare assignments, use electronic media, deliver lectures and make public presentations. Class requirements may also include field work in a variety of collaborative environments.
4. Intellectual, conceptual, integrative and quantitative abilities. A student must possess the ability to understand and read and understand documents written in English, to understand and work with measurements and calculations, and to engage in reasoning, analysis, synthesis and critical thinking. A student must be able to exercise sufficient judgment to recognize and correct performance deviations, and be able to draw on all the above mentioned abilities to be an effective problem solver, researcher, and communicator.
5. Behavioral and social attributes. A student must have the emotional health required for the full use of his or her intellectual ability. A student must be able to exercise sound judgment, and to act ethically and with integrity. He or she must develop mature, sensitive, and effective professional relationships with others. A student must be self-motivated, reliable and responsible to complete assigned tasks in a timely manner with no supervision. Students must be able to give attention to detail and have the flexibility to function in a research setting, including adapting to changes in time, place and structure of academic and research settings. The student must have the ability to work with diverse groups.
NOTE: Reasonable accommodations will be considered and may be made to qualified students who disclose a disability, so long as such accommodation does not significantly alter the essential requirements of the curriculum and the training program, or significantly affect the safety of patient care. Students who disclose that they have a disability are considered for the program if they are otherwise qualified. Qualified students with a disability who wish to request accommodations should provide the appropriate documentation of disability and submit a request for accommodation to the University’s Office for Academic Accommodations.