Master of Science in Biostatistics

Data and numbers
Master of Science In Biostatistics
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.
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.
 

Required (30 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)
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)
GPH-GU 2930 Epidemiology Design & Methods (3)


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)

Electives (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.  Examples of courses are:

*Clinical Trials:

  1. GPH-GU 2368 Applied Survival Analysis (3)
  2. GPH-GU 3220 Experimental Designs in Epidemiology (3)
  3. APSTA-GE 2013 Missing Data (2)
  4. APSTA-GE 2044 Generalized Linear Models and Extensions (2)

Machine Learning and Modeling:

  1. APSTA-GE 2047 Messy Data and Machine Learning (3)
  2. APSTA-GE 2011 Supervised and Unsupervised Machine Learning (2)
  3. APSTA-GE 2122 Applied Statistical Modeling and Inference (2)
  4. APSTA-GE 2123 Applied Statistical Modeling and Inference: Bayesian (2)

The remaining 3 credits may be selected from the electives below, or with approval of the department chair:

Electives: Statistics (choose 3 credits)
GPH-GU 3152 Advanced Agent-Based Modeling (3)
PHDSW-GS 3070 Advanced Structural Equation Modeling (3) (pre-req: PHDSW-GS 3069)
APSTA-GE 2011 Supervised and Unsupervised Machine Learning (2) (pre-reqs: APSTA-GE 2003 and APSTA-GE 2152 or equivalent R experience)
GPH-GU 2368 Applied Survival Analysis (3)
APSTA-GE 2012 Causal Inference (3) (pre-req: APSTA-GE 2004)
APSTA-GE 2094 Confirmatory Factor Analysis & Structural Equation Modeling (3) (pre-req: APSTA-GE 2003)
APSTA-GE 2044 Generalized Linear Models and Extensions (2) (pre-req: APSTA-GE 2003)
GPH-GU 2480 Longitudinal Analysis of Public Health Data (3)
APSTA-GE 2013 Missing Data (2) (pre-req: APSTA-GE 2011)
APSTA-GE 2040 Multilevel Models: Growth Curve (2) - AND - APSTA-GE 2041 Practicum in Multi-level Models (1) - note: students must earn a minimum of an A- in GPH-GU 2353/2354 in order to be eligible for these two courses.
APSTA-GE 2352 Practicum in Statistical Computing (1-2)
PHDSW-GS 3069 Structural Equation Modeling (3)

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 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 (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)
GPH-GU 5170 Introduction to Public Health  (0)
Elective (3)

Spring semester (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)


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)
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)
GPH-GU 2355 Analysis of Epidemiologic Data Using SAS (3)
Elective (3)

 

Program Comparison: MS in Biostatistics and MPH with Biostatistics concentration

 

MS Biostatistics

MPH Biostatistics Concentration

Program length

2 years

2 years

Number of credits

49

47

Requires core MPH courses

No*

Yes

STEM designation

Yes

Yes

Applied experience

None

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 2355 Analysis of Epidemiologic Data Using SAS  

 

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 2387 Survey Design, Analysis, and Reporting
GPH-GU 2286 Introduction to Data Management and Statistical Computing
GPH-GU 2225 Psychometric Measurement & Analysis in Public Health Research & Practice  
GPH-GU 2480 Longitudinal Analysis of Public Health Data

 

Other Required Courses

 

 

GPH-GU 2145 Introduction to Public Health  
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-GU2361 Research Methods in 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

Distinct focus on biostatistical and research methods.

Student develops theme for electives (e.g., clinical trials, health disparities, GIS, data visualization).

Prepares students for doctoral programs in biostatistics and other quantitative disciplines.

Prepares students to be data analysts on research studies.

General public health degree with quantitative focus.

Prepares students to work in research or practice settings, but with fewer research and epidemiology study design skills as compared to MS students.

These students will have a broader understanding of public health.

 

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