Biostatisticians who pursue the MS with a concentration in Data Science in Public Health (DSPH) apply biostatistical data analysis methods with machine learning expertise to efficiently analyze data in biomedical research. The DSPH concentration focuses on data skills that can be applied to many areas of public health research such as infectious diseases, cancer, mental health, and more. Skills acquired from the DSPH concentration are essential to biomedical research in data analyses, pattern detection in large data sets, data management, and data mining. The demand for MS Biostatistics graduates with a DSPH concentration has increased dramatically with the increase in production of large and high dimensional data. The DSPH concentration aims to prepare students to succeed in careers such as data analysts with machine learning knowledge in research institutions, pharmaceutical companies, government agencies (NIH, CDC, FDA, etc.), health insurance companies, and other organizations related to public health and/or require working with large amounts of data.
Applications for admission to the program will be consistent with the school’s graduate admissions. Eligible students will submit applications via SOPHAS Express, a centralized onlineapplication system for schools and programs of public health. Other application requirements include:
- SOPHAS express online application fee
- Transcripts of all institutions attended (with a minimum GPA of 3.0)
- A personal statement/statement of purpose (recommended length is 500 words)
- Two letters of recommendation
- A resume/CV
- TOEFL scores are required if language of instruction for prior degrees was not English
- Paper-based test: 550
- Computer administered test: 233
- Internet-based test: 60 (reading score, writing score, and listening score)
- Transcript evaluation from WES or ECE for foreign transcripts
- Fall: 8/1/2023
- Spring: 12/15/2023
Students entering the MS program are required to have a baccalaureate degree in mathematics, computer science, or equivalent.
Total credit hours: 36
General Core Course (6)
- Epidemiology in Public Health I (PUBH 7170) (3)
- Foundations of Public Health (PUBH 7180) (3)
Biostatistics/Computer Science core courses (21)
- Biostatistics Methods I (PUBH 7150) (3)
- Biostatistics Methods II (PUBH 7152) (3)
- Applied Categorical Data Analysis (PUBH 7311) (3)
- Mixed Model Regression Analysis (PUBH 7310) (3)
- Machine learning (COMP 7745) (3)
- Database systems (COMP 7116) (3)
- Data management (PUBH 7190) (3)
Elective courses (6)
- Courses focusing on computing are qualified elective courses. Following are some options:
- Bayesian Inference – MATH 7680 (3)
- Biostatistics in Bioinformatics – PUBH 7153 (3)
- Spatial Analysis and Simulation for Urban Health (PUBH 7300) (3)Statistical Programming with R (MATH 7608) (3)
- Biostatistics in data mining (PUBH 7006) (3)
- Data mining (COMP 7118) (3)
- Applied Survival Analysis in Public Health (PUBH 7309) (3)
- Algorithms in Computational Biology and Bioinformatics (COMP 7295) (3)
Comprehensive Exam (for students taking the Exam route; 3 credit hours)
Material in the following three courses will be included in a Comprehensive Exam:
- PUBH 7310 (Mixed Model Regression Analysis)
- PUBH 7311 (Applied Categorical Data Analysis)
- COMP 7745 (Machine Learning). In addition, students are required to take PUBH 7985 Practicum.
This is not required for students who take the Exam route.
- PUBH 7996
Recommended Course Sequence
Click here to view Recommended Course Sequence (PDF).
For frequently asked questions about the this program, please visit our MS FAQ.
Coordinator, Recruitment and Admissions