Biostatistics (MS)

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. 
  • International students requiring a visa to attend NYU must complete the IELTS exam in person at an authorized test center. If you are required to take the exam but will not need it for visa purposes you may choose to take it online or at a test center.

Program Requirements

¹

For all selective courses, one course is required, and the other(s) may be taken as an elective(s).

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. Please use this Graduate Elective Substitution form to request approval for courses not on this list. 

Electives Course List

²

Must take GPH-GU 2338 Machine Learning in Public Health first.

³

Requires approval of the Chair.

⁴

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.

Sample Plan of Study

Full-Time

Part-Time

¹

Electives range from 1-3 credits, but the total number of elective credits required is 12.

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

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.