MS in Public Health Data Science

About the Program

DELIVERY METHOD: FULLY ONLINE
ENROLLMENT TYPE: FULL-TIME | PART-TIME
SCHOLARSHIPS AVAILABLE

The Master of Science in Public Health Data Science (MS-PHDS) prepares students to improve population health through the responsible design, implementation, and evaluation of advanced statistical, computational, and artificial intelligence methods. The program combines the foundations of epidemiology and biostatistics with modern data science and machine learning to equip graduates with the skills to address complex public health challenges while advancing health equity.

Delivery method: Designed for accessibility and flexibility, this fully online program can be completed full or part-time through synchronous and/or asynchronous coursework. Sample class schedules, including modality details, can be found here. Find answers to common questions about our online programs and delivery methods here.

Connect with our Admissions Team!

Text: (646) 664-1366
Phone: (646) 664-8355
Email: admissions@sph.cuny.edu
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Is this degree right for you?

This program is designed for students interested in becoming data-driven leaders in public health, healthcare, research, and health technology sectors using responsible and explainable AI, reproducible analytic workflows, cloud-native computing, and effective communication of complex findings to diverse audiences. Graduates will be prepared to leverage data and technology to improve population health outcomes.

What will you learn?

The curriculum integrates the scientific foundations of epidemiology and biostatistics with modern data science, machine learning, bioinformatics, and scalable computing systems. Students will acquire the technical expertise required to analyze complex public health and biomedical data, build reproducible analytics infrastructures, and translate findings into actionable insights for public health decision-making.

Where to after graduation?

Graduates of this program will be well positioned to pursue roles such as Public Health Data Scientist, Health Data Analyst, Computational Epidemiology Analyst, Machine Learning Analyst, and Biostatistics/Data Science Analyst across governmental agencies, health systems, research institutions, and health technology organizations.

Admissions Information

Admissions Requirements

Completed SOPHAS application
Undergraduate degree from an accredited university with GPA (overall and major) of at least 3.0 preferred.
GRE optional
Personal statement/statement of purpose (recommended length is 500 words)
Background in the field: paid or volunteer experience in public health or related field
Resume
2 Letters of recommendation
TOEFL scores are required if language of instruction for prior degrees was not English
Transcript evaluation from WES or ECE for foreign transcripts.
Strong evidence of quantitative preparation, such as an undergraduate major in mathematics, statistics, computer science, physics, engineering, or significant experience in programming or data analysis

Application Deadlines

SOPHAS applications for 2027 will open on August 13, 2026.

Spring 2027 application deadline: December 1, 2026 (priority scholarship deadline)
Fall 2027 application deadline: March 1, 2027 (priority scholarship deadline)

Curriculum

Foundational Knowledge (0 credits)	PUBH 601 – Foundations of Public Health Knowledge	0
Concentration Coursework (27 credits)	BIOS 620 – Applied Biostatistics I	3
	BIOS 621 – Applied Biostatistics II	3
	EPID 620 – Epidemiological Methods I	3
	EPID 636 – Database Design and Use	3
	EPID 637 – Data Visualization and Communication	3
	BIOS 628 – Tools of Data Science	3
	BIOS 629 – Causal Inference as Data Science	3
	BIOS 630 – Principles of Machine Learning	3
	BIOS 631 – Principles of Bioinformatics	3
Culminating Experience (3 credits)	PUBH 698 – Capstone Project	3
Electives (6 credits)	Elective Coursework	6
Total Credits Required	36

Recommended Course Sequence

These sequences are recommended for full-time students. Part-time students are encouraged to meet with a staff advisor to map out an appropriate plan of study.

For students beginning their program during the fall semester:

Semester and Year	Course
Year 1 Fall	PUBH 601 – Foundations of Public Health Knowledge
	BIOS 628 – Tools of Data Science
	EPID 620 – Epidemiological Methods I
	BIOS 620 – Applied Biostatistics I
	EPID 637 – Data Visualization and Communication
Year 1 Spring	BIOS 629 – Causal Inference as Data Science
	EPID 636 – Database Design and Use
	BIOS 621 – Applied Biostatistics II
	BIOS 631 – Principles of Bioinformatics
Year 2 Fall	PUBH 698 – Capstone Project
	BIOS 630 – Principles of Machine Learning
	Elective I
	Elective II

For students beginning their program during the spring semester:

Semester and Year	Course
Year 1 Spring	PUBH 601 – Foundations of Public Health Knowledge
	EPID 620 – Epidemiological Methods I
	BIOS 620 – Applied Biostatistics I
	EPID 636 – Database Design and Use
	BIOS 631 – Principles of Bioinformatics
Year 2 Fall	BIOS 628 – Tools of Data Science
	EPID 637 – Data Visualization and Communication
	BIOS 621 – Applied Biostatistics II
	BIOS 630 – Principles of Machine Learning
Year 2 Spring	PUBH 698 – Capstone Project
	BIOS 629 – Causal Inference as Data Science
	Elective I
	Elective II

Competencies

Concentration Competencies:

Apply machine learning algorithms to analyze complex public health data
Apply statistical methods and bioinformatic algorithms to analyze biological and biomedical data
Architect and deploy reproducible, scalable data engineering pipelines and autonomous workflows
Ensure secure and responsible governance of data and AI in the deployment of health data systems
Communicate data-driven insights through reproducible visualizations and dashboards