Accepting enrollments from Tulane undergraduates now. Open to all in Fall 2027.
This program is designed for students with analytical curiosity and an interest in quantitative approaches to research and problem-solving. While there are no formal mathematics or statistics prerequisites, the curriculum is data-driven and quantitatively rigorous. Students will engage in statistical analysis, modeling, and interpretation of complex datasets. Applicants are encouraged to have some prior exposure to quantitative reasoning, statistics, or data analysis, which may come from coursework, research, or professional experience. The program provides structured support to help motivated students succeed in a quantitative environment. This degree provides students with in-demand applied analytical skills that can be applied in healthcare, pharmaceuticals, health policy, the financial sector, business, and many other industries!
Tulane undergraduates from all majors may apply to this program beginning in the second semester of their junior year. In consultation with an advisor, students would begin taking graduate courses in their senior year. The accelerated master's program allows students to apply nine credits of graduate courses to both the bachelor’s degree and the MS degree. The degree may be completed in approximately one year following graduation, depending on the number of credits students complete per semester.
This is an on-campus degree program, although some courses may be delivered online.
- Applicants must meet the school's admission and application requirements for entrance into master's programs at the WSPH. Tulane undergraduates are only required to submit one letter of recommendation, and recommendations are waived for Tulane public health majors and public minors.
- A minimum cumulative undergraduate GPA required for admission is 3.0 on a 4.0 scale. Applicants with a GPA below the 3.0 minimum may be considered for provisional admission if they demonstrate strong potential through factors such as significant professional experience, outstanding letters of recommendation, or successful completion of relevant graduate-level coursework.
Degree Program Competencies & Requirements
Program Competencies
- Apply data modeling techniques to analyze public health, medicine, and genomics data.
- Assess model performance and validity using appropriate metrics, diagnostic techniques, and cross-validation approaches.
- Utilize statistical packages (R, SAS, and STATA) to manage, process, and analyze large-scale datasets.
Degree Program Requirements
Foundational Course Requirements (9 credits):
SPHL 6020 Foundations in Public Health (3 credits)*
SPHL 6050 Biostatistics for Public Health (3 credits)
SPHL 6060 Epidemiology for Public Health (3 credits)
*can waive if student has a BSPH
Program Course Requirements (15 credits):
BIOS 6290 Data Management and Statistical Computing (3 credits)
BIOS 6220 Database Management (3 credits)
BIOS 7000 Comparative Analysis: Parametric and Non-Parametric Methods (3 credits)
BIOS 7020 Data Modeling with Regression (3 credits)
BIOS 7030 Supervised and Unsupervised Methods (3 credits)
Elective Courses (6 credits, choose 2 in consultation with faculty advisor):
BIOS 7140 Sampling and Clinical Trials Methods (3 credits)
SPHL 6110 Intro to GIS for Public Health (3 credits)
BIOS 7110 Time-to-event and longitudinal data analysis (3 credits)
BIOS 7130 Mediation, Moderation, and Multivariate Methods (3 credits)
Additional Program Requirements:
Master's Thesis Research (1 credit)
The MS thesis is the culminating experience of the MS in Data Modeling and Analytics program, allowing students to integrate and apply the knowledge and skills acquired throughout their coursework. Working individually, students will engage in a comprehensive, real-world project that addresses a complex data-driven problem drawn from industry, government, healthcare, or academic research.
Students will be expected to:
- Identify and define a research question or applied problem.
- Manage and prepare data for analysis using advanced database and querying techniques.
- Apply appropriate statistical, computational, and machine learning models to analyze the data.
- Critically evaluate the performance and limitations of chosen models.
- Communicate results through a written report.
Accelerated Master's Program Model Schedule
Fall Semester, Senior Undergraduate
SPHL 6050 Biostatistics for Public Health (3)
BIOS 6290 Data Management and Statistical Computing (3)
Semester Sub-Total: 6
Spring Semester, Senior Undergraduate
BIOS 7020 Data Modeling with Regression (3)
Semester Sub-Total: 3
Summer Semester, Year 1
SPHL 6060 Epidemiology for Public Health (3)*
SPHL 6020 Foundations in Public Health (3)
Semester Sub-Total: 6
*Students who receive a BSPH degree can waive SPHL 6020 and replace the 3 credits with an elective course
Fall Semester, Year 1
BIOS 7030 Supervised and Unsupervised Methods (3)
Two of these four electives (6)
BIOS 7110 Time to Event and Longitudinal Data Analysis
BIOS 7130 Mediation, Moderation, and Multivariate Methods
BIOS 7140 Sampling and Clinical Trials Methods
SPHL 6110 Intro to GIS for Public Health
Semester Sub-Total: 9
Spring Semester, Year 1
BIOS 6220 Database Management (3)
BIOS 7000 Comparative Analysis: Parametric and Non-Parametric Methods (3)
BIOS 9980 MS Thesis (1)
Semester Sub-Total: 7
Total Degree Credits 31
Model Schedule for Fall Entry
Fall Semester, Year 1
SPHL 6050 Biostatistics for Public Health (3)
BIOS 6290 Data Management and Statistical Computing (3)
SPHL 6020 Foundations in Public Health (3) *
Semester Sub-Total: 9
*Students who receive a BSPH degree can waive SPHL 6020 and replace the 3 credits with an elective course
Spring Semester, Year 1
BIOS 6220 Database Management (3)
BIOS 7000 Comparative Analysis: Parametric and Non-Parametric Methods (3)
BIOS 7020 Data Modeling with Regression (3)
Semester Sub-Total: 9
Summer Semester, Year 1
SPHL 6060 Epidemiology for Public Health (3)
Semester Sub-Total: 3
Fall Semester, Year 2
BIOS 7030 Supervised and Unsupervised Methods (3)
BIOS 9980 MS Thesis (1)
Two of these four electives (6)
BIOS 7110 Time to Event and Longitudinal Data Analysis
BIOS 7130 Mediation, Moderation, and Multivariate Methods
BIOS 7140 Sampling and Clinical Trials Methods
SPHL 6110 Intro to GIS for Public Health
Semester Sub-Total: 10
Total Degree Credits 31
*Students who receive a BSPH degree can waive SPHL 6020 and replace the 3 credits with an elective course
Department Interim Chair: Sudesh Srivastav, PhD
Program Director: Arti Shankar, PhD
Administrative Program Coordinator: Farhana Chaudhry
Email: bios@tulane.edu
Phone: (504) 988-2042
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