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Biostatistics (MS)

New York University
New York , United States
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About Program

Program Overview

Biostatistics (MS)

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.


Taxonomy Codes

  • NYSED: 40032
  • HEGIS: 0419.00
  • CIP: 26.1102

Overview

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.

Admissions

All applicants are required to submit the following:


  • SOPHAS application form, select a single area of concentration
  • Official transcripts from each institution attended (or an evaluation of your credentials if you graduated from a foreign institution)
  • Three letters of recommendation
  • Personal statement
  • Resume/CV
  • English language proficiency exam (TOEFL iBT or IELTS Academic) results for all applicants whose native language is not English and who did not receive the equivalent of a US bachelor's degree at an institution where English is the primary language of instruction.

Program Requirements

Course List


  • Course: GPH-GU 2106, Title: Epidemiology, Credits: 3
  • Course: GPH-GU 2995, Title: Biostatistics for Public Health, Credits: 3
  • Course: GPH-GU 2353, Title: Regression I: Linear Regression and Modeling, Credits: 3
  • Course: GPH-GU 2354, Title: Regression II: Categorical Data Analysis, Credits: 3
  • Course: GPH-GU 2361, Title: Research Methods in Public Health, Credits: 3
  • Course: GPH-GU 2450, Title: Intermediate Epidemiology, Credits: 3
  • Course: GPH-GU 5170, Title: Introduction to Public Health, Credits: 0 Selective Courses:
  • Select one of the following:
    • Course: GPH-GU 2286, Title: Introduction to Data Management and Statistical Computing, Credits: 3
    • Course: GPH-GU 2182, Title: Statistical Programming in R, Credits: 3
  • Select one of the following:
    • Course: GPH-GU 2225, Title: Psychometric Measurement and Analysis in Public Health Research and Practice, Credits: 3
    • Course: GPH-GU 2387, Title: Survey Design, Analysis, and Reporting, Credits: 3
  • Select one of the following:
    • Course: GPH-GU 2480, Title: Longitudinal Analysis of Public Health Data, Credits: 3
    • Course: GPH-GU 2368, Title: Applied Survival Analysis, Credits: 3
  • Select one of the following:
    • Course: GPH-GU 2930, Title: Epidemiological Methods and Design, Credits: 3
    • Course: GPH-GU 3225, Title: Statistical Inference, Credits: 3
    • Course: GPH-GU 2363, Title: Causal Inference: Design and Analysis, Credits: 3
    • Course: APSTA-GE 2012, Title: Causal Inference, Credits: 3 Electives: 12 credits Culminating Experience:
  • Course: GPH-GU 2686, Title: Thesis I: Practice ​and Integrative Learning Experiences, Credits: 2
  • Course: GPH-GU 2687, Title: Thesis II: Practice ​and Integrative Learning Experiences, Credits: 2 Total Credits: 46

Electives

9 credits are required to have statistical content. Students are encouraged to consider electives that are focused in a particular area, such as clinical trials, statistical genetics, or machine learning, as just a few examples. The remaining 3 credits may be in a subject that requires biostatistics (e.g., genetics). The following list contains approved elective courses:


  • Course: GPH-GU 3152, Title: Advanced Agent-Based Modeling, Credits: 3
  • Course: DS-GA 1019, Title: Advanced Python for Data Science, Credits: 3
  • Course: GPH-GU 2372, Title: Applied Bayesian Analysis in Public Health, Credits: 3
  • Course: APSTA-GE 2015, Title: Applied Spatial Statistics, Credits: 2
  • Course: GPH-GU 2368, Title: Applied Survival Analysis, Credits: 3
  • Course: DS-GA 1004, Title: Big Data, Credits: 3
  • Course: CUSP-GX 8083, Title: Big Data Management & Analysis, Credits: 3
  • Course: GPH-GU 2235, Title: Biostatistical Consulting, Credits: 3
  • Course: GPH-GU 2363, Title: Causal Inference: Design and Analysis, Credits: 3
  • Course: APSTA-GE 2012, Title: Causal Inference, Credits: 3
  • Course: GPH-GU 2336, Title: Critical Reading of the Biostatistical Literature, Credits: 3
  • Course: GPH-GU 2233, Title: Data, A I, and the People's Health, Credits: 3
  • Course: GPH-GU 2380, Title: Data-Driven Decision Making in Global Public Health, Credits: 3
  • Course: APSTA-GE 2017, Title: Databases and Data Science Practicum, Credits: 2
  • Course: APSTA-GE 2331, Title: Data Science for Social Impact, Credits: 3
  • Course: ECE-GY 7123, Title: Deep Learning, Credits: 3
  • Course: CS-GY 6953, Title: Deep Learning, Credits: 3
  • Course: GPH-GU 2930, Title: Epidemiological Methods and Design, Credits: 3
  • Course: DS-GA 1011, Title: Fundamentals of Natural Language Processing, Credits: 3
  • Course: URPL-GP 2618, Title: Geographic Information Systems and Analysis, Credits: 3
  • Course: GPH-GU 2126, Title: Healthcare Claims Data Analysis, Credits: 3
  • Course: GPH-GU 2244, Title: Health Care Management Science, Credits: 3
  • Course: GPH-GU 2324, Title: Infectious Disease Epidemiology, Credits: 3
  • Course: GPH-GU 2152, Title: Introduction to Agent-Based Modeling, Credits: 3
  • Course: GPH-GU 2286, Title: Introduction to Data Management and Statistical Computing, Credits: 3
  • Course: APSTA-GE 2110, Title: Large Databases in Applied Research, Credits: 3-4
  • Course: GPH-GU 2480, Title: Longitudinal Analysis of Public Health Data, Credits: 3
  • Course: DS-GA 1003, Title: Machine Learning, Credits: 3
  • Course: GPH-GU 2338, Title: Machine Learning in Public Health, Credits: 3
  • Course: APSTA-GE 2013, Title: Missing Data, Credits: 2
  • Course: APSTA-GE 2094, Title: Modern Approaches in Measurement, Credits: 3
  • Course: GPH-GU 2274, Title: Outbreak Epidemiology: Re-emerging and Emerging Infectious Diseases, Credits: 3
  • Course: DS-GA 1018, Title: Probabilistic Time Series Analysis, Credits: 3
  • Course: DS-GA 1007, Title: Programming for Data Science, Credits: 3
  • Course: GPH-GU 2225, Title: Psychometric Measurement and Analysis in Public Health Research and Practice, Credits: 3
  • Course: GPH-GU 2022, Title: SAS for Beginners: Data Management and Exploration, Credits: 1
  • Course: ECE-GY 9343, Title: Sel Top: Telecom Network, Credits: 3
  • Course: GPH-GU 2366, Title: Sequential Methods in Clinical Trials, Credits: 3
  • Course: GPH-GU 2198, Title: Simulations in Biostatistics, Credits: 2
  • Course: GPH-GU 2512, Title: Special Topics: Applied Spatial Statistics for Public Health, Credits: 1
  • Course: DS-GA 3001, Title: Special Topics in Data Science, Credits: 3
  • Course: GPH-GU 3225, Title: Statistical Inference, Credits: 3
  • Course: GPH-GU 2378, Title: Statistical Methods in Genomics and Bioinformatics, Credits: 3
  • Course: GPH-GU 2182, Title: Statistical Programming in R, Credits: 3
  • Course: APSTA-GE 2014, Title: Stats Analysis of Networks, Credits: 3
  • Course: PHDSW-GS 3069, Title: Structural Equation Modeling, Credits: 3
  • Course: GPH-GU 2387, Title: Survey Design, Analysis, and Reporting, Credits: 3
  • Course: DS-GA 1015, Title: Text as Data, Credits: 3
  • Course: GPH-GU 2105, Title: Thinking Critically and Ethically in Public Health, Credits: 1.5
  • Course: GPH-GU 2137, Title: Topics in Dynamic Modeling, Credits: 3
  • Course: BI-GY 7633, Title: Transcriptomics, Credits: 3

Sample Plan of Study

Full-Time

Plan of Study Grid


  • 1st Semester/Term:
    • Course: GPH-GU 2106, Title: Epidemiology, Credits: 3
    • Course: GPH-GU 2995, Title: Biostatistics for Public Health, Credits: 3
    • Course: GPH-GU 2286 or GPH-GU 2182, Title: Introduction to Data Management and Statistical Computing or Statistical Programming in R, Credits: 3
    • Course: GPH-GU 5170, Title: Introduction to Public Health, Credits: 0
    • Elective Course, Credits: 3
  • 2nd Semester/Term:
    • Course: GPH-GU 2353, Title: Regression I: Linear Regression and Modeling, Credits: 3
    • Course: GPH-GU 2361 or GPH-GU 5361, Title: Research Methods in Public Health or Research Methods in Public Health, Credits: 3
    • Course: GPH-GU 2450, Title: Intermediate Epidemiology, Credits: 3
    • Elective Course, Credits: 3
  • 3rd Semester/Term:
    • Course: GPH-GU 2686, Title: Thesis I: Practice ​and Integrative Learning Experiences, Credits: 2
    • Course: GPH-GU 2354, Title: Regression II: Categorical Data Analysis, Credits: 3
    • Course: GPH-GU 2930 or GPH-GU 3225 or GPH-GU 2363 or APSTA-GE 2012, Title: Epidemiological Methods and Design or Statistical Inference or Causal Inference: Design and Analysis or Causal Inference, Credits: 3
    • Course: GPH-GU 2225 or GPH-GU 2387, Title: Psychometric Measurement and Analysis in Public Health Research and Practice or Survey Design, Analysis, and Reporting, Credits: 3
  • 4th Semester/Term:
    • Course: GPH-GU 2687, Title: Thesis II: Practice ​and Integrative Learning Experiences, Credits: 2
    • Course: GPH-GU 2480 or GPH-GU 2368, Title: Longitudinal Analysis of Public Health Data or Applied Survival Analysis, Credits: 3
    • Elective Course, Credits: 3
    • Elective Course, Credits: 3 Total Credits: 46

Part-Time

Plan of Study Grid


  • 1st Semester/Term:
    • Course: GPH-GU 2106, Title: Epidemiology, Credits: 3
    • Course: GPH-GU 2995, Title: Biostatistics for Public Health, Credits: 3
    • Course: GPH-GU 5170, Title: Introduction to Public Health, Credits: 0
  • 2nd Semester/Term:
    • Course: GPH-GU 2353, Title: Regression I: Linear Regression and Modeling, Credits: 3
    • Course: GPH-GU 2450, Title: Intermediate Epidemiology, Credits: 3
  • 3rd Semester/Term:
    • Course: GPH-GU 2286 or GPH-GU 2182, Title: Introduction to Data Management and Statistical Computing or Statistical Programming in R, Credits: 3
    • Course: GPH-GU 2354, Title: Regression II: Categorical Data Analysis, Credits: 3
  • 4th Semester/Term:
    • Course: GPH-GU 2361 or GPH-GU 5361, Title: Research Methods in Public Health or Research Methods in Public Health, Credits: 3
    • Elective 1, Credits: 3
  • 5th Semester/Term:
    • Course: GPH-GU 2387 or GPH-GU 2225, Title: Survey Design, Analysis, and Reporting or Psychometric Measurement and Analysis in Public Health Research and Practice, Credits: 3
    • Elective 1, Credits: 3
  • 6th Semester/Term:
    • Course: GPH-GU 2480 or GPH-GU 2368, Title: Longitudinal Analysis of Public Health Data or Applied Survival Analysis, Credits: 3
    • Elective 1, Credits: 3
  • 7th Semester/Term:
    • Course: GPH-GU 2686, Title: Thesis I: Practice ​and Integrative Learning Experiences, Credits: 2
    • Course: GPH-GU 2930 or GPH-GU 3225 or GPH-GU 2363 or APSTA-GE 2012, Title: Epidemiological Methods and Design or Statistical Inference or Causal Inference: Design and Analysis or Causal Inference, Credits: 3
  • 8th Semester/Term:
    • Course: GPH-GU 2687, Title: Thesis II: Practice ​and Integrative Learning Experiences, Credits: 2
    • Elective 1, Credits: 3 Total Credits: 46

Learning Outcomes

Upon completion of the Biostatistics Master of Science degree, graduates will have the skills and competencies to:


  1. Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
  2. Harness basic concepts of probability, random variation and commonly used statistical probability distributions.
  3. Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
  4. Implement the appropriate analytic methods for calculating key measures of association.
  5. Understand and apply ethical principles to data acquisition, management, storage, sharing, and analysis
  6. Interpret results of statistical analyses found in public health research studies.
  7. Utilize relevant statistical software for data analysis.

Policies

Program Policies

Waiver Exam

The computing requirement for MPH and MS students in Biostatistics is the successful completion of GPH-GU 2182 Statistical Programming in R or GPH-GU 2286 Introduction to Data Management and Statistical Computing. This requirement must be completed in the first year of the degree program. Students who feel they know the material in GPH-GU 2182 Statistical Programming in R sufficiently well are eligible to take an online exam to waive one or both of the courses.


NYU Policies

University-wide policies can be found on the New York University Policy pages.


School of Global Public Health Policies

A list of related academic policies can be found on the School of Global Public Health academic policies page.


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