Master's Program in Data Science
Overview
The Master of Science in Data Science (MSDS) at Tulane University is an interdisciplinary program jointly offered by the Mathematics and Computer Science departments. The program integrates mathematics, statistics, computation, and data-driven methodology to prepare students for modern careers in data science, machine learning, and analytics across industry, government, and research sectors.
Students are trained in probability, statistical modeling, algorithms, data management, machine learning, and scientific computing, developing both theoretical foundations and hands-on expertise. The MSDS program emphasizes a rigorous quantitative core combined with flexible electives that allow students to specialize in advanced computational and applied areas.
The MS graduate advisors in Data Science (see the contact below) work closely with each student to design a personalized plan of study aligned with academic preparation and career goals.
Paths to a Tulane Master’s in Data Science
The program accommodates several types of students:
• 4+1 MSDS students, who complete both a Tulane B.S. and M.S. in five years.
• Full-time students, who typically complete the MSDS program in three to four semesters.
Students who begin as Ph.D. students in Mathematics or Computer Science are not eligible for the standalone MSDS program.
Resources
Graduates of the MSDS program are well prepared for:
• Data scientist and machine learning engineer roles in technology, finance, healthcare, and industry.
• Positions involving statistical modeling, predictive analytics, and large-scale data processing.
• Graduate study in data science, computer science, statistics, applied mathematics, or related fields.
• Technical careers in artificial intelligence, software engineering, computational science, and research.
A graduating MSDS student will acquire strong foundations in probability, inference, linear algebra, algorithmic thinking, machine learning, and modern data science workflows. The program emphasizes computational proficiency, practical modeling skills, and interdisciplinary applications.
Students design their coursework plan in consultation with faculty advisors and the Graduate Studies Committee (GSC) to ensure timely progress and compliance with School of Science and Engineering (SSE) policy.
The MSDS is a 33-credit, non-thesis program consisting of:
• 9 credits of Foundations
• 12 credits of Core Data Science courses
• 12 credits of Electives
The MSDS program does not include a thesis option.
Data Science Foundations (9 credits)
• Math 6070: Introduction to Probability
• Math 6080: Introduction to Statistical Inference
• Math 6090: Linear Algebra
Data Science Core (12 credits)
• Math 6040/7260: Linear Models
• CMPS 6100: Introduction to Computer Science
• CMPS 6790: Data Science
• CMPS 6240/6720: Machine Learning
Data Science Electives (12 credits)
Students select four courses from approved electives, including:
Mathematics and Statistics Electives:
• Math 7360: Data Analysis
• Math 6030/7030: Stochastic Processes
• Math 6370/7370: Time Series Analysis
• Math 6310–7570: Scientific Computation I–II
• Math 7770: Algebraic Coding Theory
Computer Science Electives:
• COSC 6000: C++ Programming for Science and Engineering
• COSC 6200: Large Scale Computation
• CMPS 6360: Data Visualization
• CMPS 6260: Advanced Algorithms
• CMPS 6140/6620: Artificial Intelligence
• CMPS 6660: Deep Learning
• CMPS 6610: Algorithms
• CMPS 6650: Computer Vision
• CMPS 6730: Natural Language Processing
• CMPS 6740: Reinforcement Learning
Approved Interdisciplinary Electives:
• BIOS 7150: Categorical Data Analysis
• BIOS 7300: Survival Data Analysis
• EBIO 6440: Introduction to Data Science for Ecologists
• BMEN 6800: Data Science: Medical Imaging / Machine Learning
Notes:
• Students with strong prior backgrounds may, with GSC approval, substitute advanced electives for Foundations courses.
• With prior GSC approval, up to 6 credit hours may be taken in other Tulane departments.
(see the Mathematics Graduate Handbook for further details)
• Following SSE policy, all graduate students must maintain a minimum 3.0 (B) GPA.
• Grades: One B– triggers probation consideration; two B– grades or one grade below B– results in probation and possible dismissal. No course with a grade below B– counts toward the degree.
• Up to 6 transfer credit hours may be applied toward the MSDS degree with GSC approval.
• Students must maintain continuous registration until the degree is conferred.
• Students must adhere to the Unified Code of Graduate Student Academic Conduct.
Applicants must hold a bachelor’s degree in mathematics, statistics, computer science, engineering, or a closely related discipline. Admission is competitive and based on academic preparation in both quantitative reasoning and computation.
Minimum Requirements:
1. GPA of 3.0 or higher (on a 4.0 scale).
2. Evidence of preparation in:
- Calculus and Multivariable Calculus
- Linear Algebra
- Probability or Statistics
- At least one programming course (recommended: Python, C++, Java)
- Additional background in algorithms or data structures is recommended
Application Materials:
• Transcripts from all colleges/universities attended
• Personal statement describing academic interests and goals
• At least one letter of recommendation (optional but recommended)
4+1 Tulane Applicants:
• Minimum 3.5 GPA and adequate preparation in mathematics, statistics, and computer science coursework
Current tuition and fee information is available at:
studentaccounts.tulane.edu/tuition-and-fees
Applications are submitted online through the Tulane Graduate Application System. For questions regarding admission applicants should contact Prof. Marie Dahleh, mdahleh@tulane.edu; for academic inquiries regarding the M.S. Programs in Data Science please contact Prof. Rafal Komendarczyk, rako@tulane.edu.
