The goal of the MS in Health Data Science program is to prepare students to work as a health data scientist in academia or health care industries. Upon completion of the MS in Health Data Science the students will have an extensive understanding of biostatistical principles and computing skills with applications in clinical and other diverse data resources.
At the completion of the MS program in Health Data Science the graduate should be able to:
- Demonstrate a broad knowledge and understanding of diverse resources and types of data
- A broad understanding of statistical principles, computing skills and apply them in health outcome data.
- Function as a collaborator on a research team.
- Assume responsibility for the computing and data visualization analyses for health science investigations.
- Assist with the design and implementation of data management systems for large health science studies.
- Prepare reports and publications resulting from health science studies.
With the rising emphasis on highly personalized data in health care, pharmaceutical, insurance and other organizations, so rises the need for health data scientists. 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 for data scientists. Our program will bring statistics and data science 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 degree typically compliments prior education and careers in:
- Pharmaceutical Industry
- Health Care
- Insurance Company
- Consulting
- Education
- Health Analytics
- Health Research
- Government
- Biotechnology
Fortune magazine ranked statistics and biostatistics among the top graduate degrees in 2015 and 2016 based on salary, growth and job satisfaction. Graduating with this degree will open new doors to a more rewarding career.
The application for admission to the M.S. in Health Data Science 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 in statistics, biostatistics, mathematics or applied mathematics 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 they meet the Minimum English Proficiency Requirement.
- Letter grade of B or better in Calculus I and II (or the equivalent).
- 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.
Applicants will be assessed based on these requirements.
The program consists of 36 credit hours including annual evaluations and the successful completion of the Master’s Comprehensive Examination.
This 36-credit-hour program is organized into four sections: required statistics foundation, required computing foundation, required health data science foundation and an elective designed to equip students with skills in statistical and computational methods for the acquisition and analysis of Big Data. Students are required to maintain a GPA of 3.0 on a 4.0 scale.
Required Statistics Foundation Courses (12 semester credit hours (SCH))
Course List | Code | Title | Hours |
| HDSC 805 | Professionalism, Ethics and Leadership in the Statistical Sciences | 3 |
| HDSC 835 | Categorical Data Analysis | 3 |
| HDSC 840 | Linear Regression | 3 |
| HDSC 845 | Survival Analysis | 3 |
Required Computing Foundation Courses (6 semester credit hours (SCH))
Course List | Code | Title | Hours |
| HDSC 818 | Introduction to R | 1 |
| HDSC 819 | Introduction to Python | 1 |
| HDSC 822 | Introduction to SQL | 1 |
| HDSC 823 | Introduction to Programming and Applied Statistics in R | 3 |
Required Health Data Science Foundation Courses (12 semester credit hours (SCH))
Course List | Code | Title | Hours |
| HDSC 824 | Data Visualization and Acquisition | 3 |
| HDSC 861 | Observational Health Data Analysis | 3 |
| HDSC 880 | Data Mining and Analytics | 3 |
| HDSC 881 | Statistical Learning I | 3 |
Electives (6 semester credit hours (SCH))
Course List | Code | Title | Hours |
| HDSC 815 | Introduction to Bioinformatics | 3 |
| HDSC 820 | SAS Programming I | 3 |
| HDSC 830 | Experimental Design | 3 |
| HDSC 855 | Statistical Methods in Genomics Research | 3 |
| HDSC 882 | Statistical Learning II | 3 |
To graduate with a MS in Health Data Science, students must pass the Master’s Comprehensive Examination. The Masters Comprehensive Examination is administered after a student has successfully completed: (1) the required coursework including Categorical Data Analysis (HDSC 835), Linear Regression (HDSC 840), Statistical Learning I (HDSC 881), and Observational Health Data Analysis (HDSC 861); and (2) during the final semester of enrollment. 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.
Because the MS in Health Data Science degree signifies that the holder is prepared for entry into the practice of biostatistics 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 MS program:
1. Observation. A student must be able to observe and evaluate class demonstrations and field experiences relevant to the field of statistics. 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.
