Master of Science in Biostatistics | NYU School of Global Public Health

MASTER OF SCIENCE IN BIOSTATISTICS

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PROGRAM CREDITS

2 years

FULL-TIME STUDY

4 years

PART-TIME STUDY

The Master of Science in Biostatistics trains students in study design, data analysis, and statistical reporting for scientific and lay audiences. Key skills include data management, statistical reasoning, the interpretation of numeric data for scientific inference, and the ability to collaborate and communicate effectively with scientists and other public health stakeholders across disciplines. Thematic areas of study include clinical trials, machine learning, and data science.

Quick Links

Degree Requirements // Full-time Course Sequence // Part-time Course Sequence // Biostatistics (MS) Requirements Checklist // Biostatistics Course Substitution Form // Program Comparison: MS to MPH // How to Apply //

A Hands-on Approach to Data

The Master of Science in Biostatistics program will train students in biostatistical methods for study design, data analysis, and statistical reporting for scientific and lay audiences. This degree will train students in key areas including data management, statistical reasoning, the interpretation of numeric data for scientific inference in studies in medicine and public health, and the ability to collaborate and communicate effectively with scientists and other public health stakeholders across disciplines. Graduates of the program are prepared to work as statisticians in a variety of professional environments including government, academic, healthcare, and industry. In addition, students receive training in preparation for quantitative doctoral programs in public health, such as biostatistics and epidemiology.

Students will have the opportunity to work with faculty on many public health problems. Examples include:

Problems of randomly timed biomarker measurements in Alzheimer’s disease cohort studies.
Selection bias due to delayed entry to cohort studies.
N-of-1 study design in Alzheimer’s disease.
Mixed-methods (qualitative/quantitative) community-engaged research focused on rigorous measurement.
Survey research for community-based interventions and health disparities research.
Implementation, evaluation, and enhancement of the infrastructure of community-engaged research
Resolution of high granularity measures of disease incidence and risk from person-generated data (social media, mobile tools, wearables, etc.)
Statistical (spatiotemporal) and machine learning methods for incorporating unstructured data in population disease modeling
Zero-inflated count models to understand the changes in count outcomes (e.g. substance use, smoking behaviors, sexual risk-taking) over time.
Time diary methodology to understand the temporal associations between daily behaviors, perceptions, of individual health.
Biological biomarkers of stress among young sexual minority men and the links between sexual minority stress and biological markers of stress.

Students are engaged in several active learning opportunities outside of their courses:

There is a journal club that meets bimonthly in which they select and present papers and lead discussion about the design and analytical issues in the papers.
There are short-courses in computing and coding, such as in Stata and R.
There is a consulting laboratory in which students are mentored in providing statistical consulting.

Public Health Internships

Biostatistics concentration MS students are strongly encouraged to conduct internships off-campus during the summer and possibly through the Fall semester. This will best prepare students for their thesis, which is a required component of our MS program. There are numerous internship opportunities available throughout NYC, and we expect that students will have many options. When searching and applying for internships, be sure to take into consideration how your experience will relate to your coursework and the aspects of your public health designation. As a note, international students must receive written authorization from the Office of Global Services (OGS), prior to their official start date.

A STEM Designated Masters Degree

The Master of Science in Biostatistics program is classified as STEM-eligible, allowing international students on an F-1 visa to apply for two years of additional employment in the United States after graduation if they meet the required criteria.

World Class Faculty

The Biostatistics faculty at NYU GPH has expertise in a broad range of biostatistical methods and substantive areas of study. Faculty have methodological expertise in survival analysis, clinical trials, statistical inference, mixed-methods community-engaged research, survey research, machine learning, methods for analysis of social media and mobile health data, intensive longitudinal designs and analysis, biomarkers, latent variables, mediation analysis and causal inference. In addition, faculty have field-proven expertise in health disparities, community-based research, stakeholder engaged research, Alzheimer’s disease, minority stress, substance use, tobacco research.

Download the PDF Program Brochure

Required (30-31 credits, including the selective courses below):

GPH-GU 2106 Epidemiology (3)
GPH-GU 2995 Biostatistics for Public Health (3)
GPH-GU 5170 Introduction to Public Health (0)
GPH-GU 2353 Regression I: Linear Regression and Modeling (3)
GPH-GU 2354 Regression II: Categorical Data Analysis (3)
GPH-GU 2361 Research Methods in Public Health (3)
GPH-GU 2450 Intermediate Epidemiology (3)

For all selective courses below, when you take one as a requirement, the other may be taken as an elective.

Choose one of the following (3-4 credits)
GPH-GU 2286 Introduction to Data Management and Statistical Computing (3)*
GPH-GU 2183 Introduction to Statistical Programming in R (2) and GPH-GU 2184 Intermediate Statistical Programming in R (2) - please note that students who choose this option will take one less elective credit

Choose one of the following (3 credits):
GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice (3)
GPH-GU 2387 Survey Design, Analysis, and Reporting (3)

Choose one of the following (3 credits):
GPH-GU 2480 Longitudinal Analysis of Public Health Data (3)
GPH-GU 2368 Applied Survival Analysis (3)

Choose one of the following (3 credits):
GPH-GU 2930 Epidemiology Design & Methods (3)
GPH-GU 3225 Statistical Inference (3)
GPH-GU 2363 Causal Inference: Design and Analysis (3)
APSTA-GE-2012 Causal Inference (3)

Electives (11-12 credits):

9 credits are required to be in an approved, thematic area (e.g., clinical trials, machine learning and modeling, data science) and must have statistical content. The remaining 3 credits may be selected from the electives below (if not included in your 9 thematic credits or as selective requirements), or with approval of the department chair. The thematic area and courses taken are flexible and to be chosen by the student, with approval by the department chair; below are two examples of themes, and relevant courses, which have been accepted in the past:

**Clinical Trials:

GPH-GU 2368 Applied Survival Analysis (3)
GPH-GU 2480 Longitudinal Analysis of Public Health Data (3)
GPH-GU 3220 Experimental Study Designs in Epidemiology (3)
APSTA-GE 2013 Missing Data (2)
GPH-GU 3225 Statistical Inference (3)

Machine Learning and Inference:

GPH-GU 2338 Machine Learning in Public Health (3)
GPH-GU 3225 Statistical Inference (3)
GPH-GU 2363 Causal Inference: Design and Analysis (3)
GPH-GU 2372 Applied Bayesian Analysis in Public Health (3)

Please note that these are just two examples. You may create your own theme for 9 elective credits.

Electives: Statistics (choose 3 credits) - see below for enrollment instructions for non-GPH course enrollment.

GPH-GU 3152/5152 Advanced Agent-Based Modeling (3)
DS-GA 1019 Advanced Python for Data Science (3) (requires approval of the Chair)
APSTA-GE 2015 Advanced Spatial Statistics (2)
PHDSW-GS 3070 Advanced Structural Equation Modeling (3) (pre-req: PHDSW-GS 3069)
CUSP-GX 6004 Advanced Topics in Urban Science and Informatics - Transportation Policymaking (3)
GPH-GU 2355 Analysis of Epidemiologic Data Using SAS (3) (requires approval of the Chair)
GPH-GU 2372 Applied Bayesian Analysis in Public Health (3)
GPH-GU 2368 Applied Survival Analysis (3)
DS-GA 1004 Big Data (3) - (requires approval of the Chair)
CUSP-GX 6002 Big Data Management and Analysis (3)
GPH-GU 2235 Biostatistical Consulting (3)
GPH-GU 2363 Causal Inference: Design and Analysis (3)
APSTA-GE 2012 Causal Inference (3) (pre-req or co-req: GPH-GU 2354 Regression II)
GPH-GU 2336 Critical Reading of the Biostatistical Literature (3)
APSTA-GE 2331 Data Science for Social Impact (requires approval of the Chair)
GPH-GU 2380 Data-Driven Decision-Making in Global Public Health (3) (requires approval of the Chair)
CS-GY 6953 Deep Learning (3) - same course as ECE-GY 7123
ECE-GY 7123 Deep Learning (3) - same course as CS-GY 6953
GPH-GU 2930 Epidemiology Design and Methods (3)
URPL-GP 2618 Geographic Systems & Analysis (3)
GPH-GU 2126 Healthcare Claims Data Analysis (3)
GPH-GU 2244 Health Care Management Science (3) (requires approval of the Chair)
GPH-GU 2324 Infectious Disease Epidemiology (3) (requires approval of the Chair)
GPH-GU 2184 Intermediate Statistical Programming in R (2)
GPH-GU 2152 Introduction to Agent-Based Modeling (3)
GPH-GU 2286 Introduction to Data Management & Statistical Computing (3)
GPH-GU 2183 Introduction to Statistical Programming in R (2)
APSTA-GE 2110 Large Databases in Applied Research (3-4)
GPH-GU 2480 Longitudinal Analysis of Public Health Data (3)
DS-GA 1003 Machine Learning (3) - (must take GPH-GU 2338 first) (requires approval of the Chair)
GPH-GU 2338 Machine Learning in Public Health (3)
APSTA-GE 2013 Missing Data (2)
APSTA-GE 2094 Modern Approaches in Measurement (3)
DS-GA 1011 Natural Language Processing with Representation Learning (3)
GPH-GU 2274 Outbreak Epidemiology Re-Emerging and Emerging Infectious Disease (3) (requires approval of the Chair)
DS-GA 1018 Probabilistic Time Series Analysis (3)
DS-GA 1007 Programming for Data Science (3)
GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice (3)
GPH-GU 2022 SAS for Beginners: Data Management and Exploration (1)
ECE-GY 9343 SEL TOP: TELECOM NETWORK (3) (requires approval of the Chair)
GPH-GU 2512 Special Topics: Applied Spatial Statistics for Public Health (1)
DS-GA 3001 Special Topics in Data Science-Advanced Python for Data Science (3) (requires approval of the Chair)
GPH-GU 3225 Statistical Inference (3)
APSTA-GE 2014 Stats Analysis of Networks (3)
PHDSW-GS 3069 Structural Equation Modeling (3)
GPH-GU 2387 Survey Design, Analysis, and Reporting (3)
DS-GA 1015 Text as Data (3)
GPH-GU 2105 Thinking Critically and Ethically in Public Health (1.5)
BI-GY 7633 Transcriptomics (3)

Non-GPH Course enrollment

For Steinhardt courses (APSTA-GE), look in the specific course's Notes section in Albert and there should be instructions on how to enroll or request a code to enroll. If there are no notes and you're unable to enroll, please reach out to steinhardt.a3sr@nyu.edu.
For Center for Urban Science & Progress classes (CUSP-GX), please reach out to cusp.education@nyu.edu.
For Data Science courses (DS-GA), please reach out to Tina Lam at tina.lam@nyu.edu.
For Tandon courses (ECE-GY or BI-GY), please reach out to Prof. Xiao-Kang Chen at xkchen@nyu.edu.

Culminating Experience (4 credits)
GPH-GU 2686 Thesis I: Practice and Integrative Learning Experiences (2)
GPH-GU 2687 Thesis II: Practice and Integrative Learning Experiences (2)

* Students who enroll in GPH-GU 2286 may not also take GPH-GU 2022 due to course content overlap. For students who want to take both courses, please note that GPH-GU 2022 will not count towards the degree requirements.
**Students in the Clinical Trials group who are taking GPH-GU 2368 Applied Survival Analysis as one of the two choices in the required courses, must take a different fourth elective.

Year 1:

Fall semester (11-12 credits)
GPH-GU 2106 Epidemiology (3)
GPH-GU 2995 Biostatistics for Public Health (3)
GPH-GU 2286 Introduction to Data Management and Statistical Computing (3) - OR - GPH-GU 2183 Introduction to Statistical Programming in R (2) AND GPH-GU 2184 Intermediate Statistical Programming in R (2) - please note that GPH-GU 2184 is also offered in January
GPH-GU 5170 Introduction to Public Health (0)
Elective (3)

January (2 credits)
GPH-GU 2184 Intermediate Statistical Programming in R (2) - students who did not take GPH-GU 2183 do not need to take this course; also note that January courses count towards the Spring course load

Spring semester (11-12 credits)
GPH-GU 2353 Regression I: Linear Regression and Modeling (3)
GPH-GU 2361 Research Methods in Public Health (3)
GPH-GU 2450 Intermediate Epidemiology (3)
Elective (3) - students who are taking GPH-GU 2184 in January do not have to take an elective in this term

Year 2:

Fall semester (11 credits)

GPH-GU 2686 Thesis I: Practice and Integrative Learning Experiences (2)
GPH-GU 2354 Regression II: Categorical Data Analysis (3)
GPH-GU 2930 Epidemiology Design & Methods (3) - OR - GPH-GU 3225 Statistical Inference (3) - OR - GPH-GU 2363 Causal Inference: Design and Analysis (3) - OR - APSTA-GE 2021 Causal Inference (3)
GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice (3) - OR - GPH-GU 2387 Survey Design, Analysis, and Reporting (3)

Spring semester (11 credits)

GPH-GU 2687 Thesis II: Practice and Integrative Learning Experiences (2)
GPH-GU 2480 Longitudinal Analysis of Public Health Data (3) - OR -
GPH-GU 2368 Applied Survival Analysis (3)
Elective (3)
Elective (2-3)

Year 1:

Fall Semester (6 credits)
GPH-GU 2106 Epidemiology (3)
GPH-GU 2995 Biostatistics for Public Health (3)
GPH-GU 5170 Introduction to Public Health (0)

Spring Semester (6 credits)
GPH-GU 2353 Regression I: Linear Regression and Modeling (3)
GPH-GU 2450 Intermediate Epidemiology (3)

Year 2:

Fall Semester (4-7 credits)
GPH-GU 2286 Introduction to Data Management and Statistical Computing (3) -OR- GPH-GU 2183 Introduction to Statistical Programming in R (2) AND GPH-GU 2184 Intermediate Statistical Programming in R (2) - GPH-GU 2184 may also be taken in January
GPH-GU 2354 Regression II: Categorical Data Analysis (3)

January Semester (2 credits)
GPH-GU 2184 Intermediate to Statistical Programming in R (2) - only needed if not already taken in the Fall along with GPH-GU 2183

Spring Semester (6 credits)
Elective (3)
GPH-GU 2361 Research Methods in Public Health (3)

Year 3:

Fall Semester (6 credits)
GPH-GU 2387 Survey Design, Analysis, and Reporting (3) -OR- GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice (3)
Elective (3)

Spring Semester (6 credits)
GPH-GU 2480 Longitudinal Analysis of Public Health (3) -OR- GPH-GU 2368 Applied Survival Analysis (3)
Elective (3)

Year 4:

Fall Semester (5 credits)
GPH-GU 2686 Thesis I: Practice and Integrative Learning Experiences (2)
GPH-GU 2930 Epidemiology Design & Methods (3) - OR - GPH-GU 3225 Statistical Inference (3) - OR - GPH-GU 2363 Causal Inference: Design and Analysis (3) - OR - APSTA-GE 2021 Causal Inference (3)

Spring Semester (5-6 credits)
GPH-GU 2687 Thesis II: Practice and Integrative Learning Experiences (2)
Elective (2-3)

Program Comparison: MS in Biostatistics and MPH with Biostatistics concentration

	MS Biostatistics	MPH Biostatistics
Program length	2 years	2 years
Number of credits	46	46
Requires core MPH courses	No*	Yes
STEM designation	Yes	Yes
Applied experience	Recommended Internship Experience	Research or Practice
Culminating project	Thesis	Thesis

Required Epidemiology Courses
GPH-GU 2106 Epidemiology	✔	✔
GPH-GU 2450 Intermediate Epidemiology	✔
GPH-GU 2930 Epidemiology Design & Methods	✔
GPH-GU 2361 Research Methods in Public Health	✔

Required Biostatistics Courses
GPH-GU 2996/5995 Biostatistics for Public Health	✔	✔
GPH-GU 2353 Regression I: Linear Regression and Modeling	✔	✔
GPH-GU 2354 Regression II: Categorical Data Analysis	✔	✔
GPH-GU 2286 Introduction to Data Management and Statistical Computing	✔	✔
GPH-GU 2387 Survey Design, Analysis, and Reporting	✔*	✔*
GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice	✔*	✔*
GPH-GU 2480 Longitudinal Analysis of Public Health Data	✔*	✔*
GPH-GU 2368 Applied Survival Analysis	✔*	✔*

* students choose GPH-GU 2387 or GPH-GU 2225 and GPH-GU 2480 or GPH-GU 2368

Other Required Courses
GPH-GU 2110/5110 Health Care Policy		✔
GPH-GU 2112/5112 Public Health Management and Leadership		✔
GPH-GU 2140/5140 Global Issues in Social and Behavioral Health		✔
GPH-GU 2153/5153 Global Environmental Health		✔
GPH-GU 2190/5190 Essentials of Public Health Biology		✔
GPH-GU 5170 Introduction to Public Health	✔
GPH-GU 5171 Global Health Informatics Workshop		✔
GPH-GU 5175 Readings in the History & Philosophy of Public Health I		✔
GPH-GU 5180 Readings in the History & Philosophy of Public Health II		✔
GPH-GU 5185 Readings in the History & Philosophy of Public Health III		✔
GPH-GU 2686 Thesis I: Practice and Integrative Learning Experiences	✔	✔
GPH-GU 2687 Thesis II: Practice and Integrative Learning Experiences	✔	✔

Distinguishing Features	In-depth training in study design, data analysis, statistical computing You develop a theme for electives that gives you focused expertise (e.g., clinical trials, health disparities, data science) Prepares you for doctoral programs in biostatistics and other quantitative disciplines	Broad training in public health, with an emphasis on biostatistical and other quantitative methods You design a mentored, applied practice experience in a public health or biomedical setting Prepares you for doctoral programs in quantitative public health
	You can work as an applied biostatistician in research or practice settings with either the MS or the MPH

* GPH-GU 5170 Introduction to Public Health, which covers the 12 MPH foundational learning objectives, is taken in place of MPH core courses.

How to Apply

Ready to take the next step in your academic and professional journey? View our application requirements to learn how to apply to the MS in Biostatistics at NYU. Need more information? From planning a campus visit to learning how to pay for your education to applying for your dream degree program, everything you need is right here.