Masters's in Data Science

The Master of Science in Data Science (MSDS) at Stevens Institute of Technology, located in Hoboken, New Jersey, is an advanced graduate program designed to develop expertise in data analytics, statistical modeling, machine learning, artificial intelligence, data management, and computational methods. Offered through the Department of Mathematical Sciences within the Charles V. Schaefer, Jr. School of Engineering and Science, the program combines mathematical foundations with practical data science applications, preparing students to analyze complex datasets, develop predictive models, and support data-driven decision-making across business, finance, technology, government, and research.

The program requires 30 graduate credits, consisting of 18 credits of core coursework and 12 credits of electives under the published 2024–2025 academic catalog. It is offered in an on-campus format, with an optional thesis opportunity and four concentration pathways: Fundamentals of Data Science, Data Acquisition and Management, Data Security, and Business Applications. Students can customize their studies according to their academic interests and professional goals.

Curriculum Structure

Year 1

Students begin by developing a strong foundation in probability theory, statistical methods, machine learning, deep learning, numerical linear algebra, and optimization. The core curriculum emphasizes mathematical and computational techniques required to collect, process, analyze, and model complex datasets.

Example coursework includes:

• Introduction to Probability Theory (MA 540)

• Statistical Methods (MA 541)

• Applied Machine Learning (CPE 595)

• Machine Learning: Fundamentals and Applications (CS 559)

• Deep Learning (CS 583)

• Numerical Linear Algebra for Big Data (MA 544)

• Optimization for Data Science (MA 576)

• Time Series Analysis I (MA 641)

Students develop practical knowledge of statistical inference, predictive modeling, mathematical optimization, machine learning algorithms, and large-scale data analysis.

Year 2

During the second year, students pursue advanced electives and specialized coursework according to their selected concentration. The program offers four concentration pathways that allow students to develop expertise in fundamental data science, data management, security, or business applications.

Available concentrations include:

• Fundamentals of Data Science: Focuses on advanced statistics, stochastic processes, network analysis, numerical methods, natural language processing, and reinforcement learning.

• Data Acquisition and Management: Emphasizes database management, cloud computing, distributed systems, big data technologies, and data exploration.

• Data Security: Covers cybersecurity, cryptography, privacy protection, enterprise security, and information systems security.

• Business Applications: Focuses on business intelligence, marketing analytics, supply chain analytics, financial data visualization, and generative AI.

Example coursework includes:

• Statistical Network Analysis (MA 577)

• Spatial and Spatio-Temporal Statistical Modeling (MA 613)

• Tensor Methods for Data Analysis (MA 617)

• Natural Language Processing (CS 584)

• Database Management Systems I (CS 561)

• Enterprise and Cloud Computing (CS 526)

• Fundamentals of Cybersecurity (CS 573)

• Marketing Analytics (BIA 672)

• Big Data Technologies (BIA 678)

Students can also pursue an optional Master of Science Thesis (MA 900) worth six credits, subject to program coordinator approval, allowing them to investigate a specific data science problem under faculty guidance.

Focus Areas

Data Science; Data Analytics; Artificial Intelligence; Machine Learning; Deep Learning; Statistical Modeling; Probability Theory; Data Acquisition and Management; Data Security; Business Intelligence; Big Data Analytics; Database Management; Natural Language Processing; Predictive Modeling; Numerical Optimization; Time Series Analysis; Statistical Computing; Cloud Computing; Data Visualization; Reinforcement Learning.

Learning Outcomes

Develop expertise in data collection, data processing, probability theory, statistical analysis, machine learning, deep learning, predictive modeling, mathematical optimization, database management, big data technologies, natural language processing, data visualization, cybersecurity, business intelligence, numerical computation, and data-driven decision-making.

Professional Alignment (Accreditation)

The program prepares students for careers in data science, data analytics, machine learning, business intelligence, financial technology, database management, and research. Its curriculum combines mathematical foundations with advanced computational methods, enabling graduates to analyze large-scale datasets, develop predictive models, and generate meaningful insights for organizations.

Students benefit from:

• Industry-Relevant Curriculum: Advanced training in machine learning, statistical modeling, deep learning, optimization, and data management.

• Specialized Concentrations: Opportunities to develop expertise in four areas: Fundamentals of Data Science, Data Acquisition and Management, Data Security, and Business Applications.

• Research Opportunities: Access to faculty mentorship, research laboratories, interdisciplinary projects, and optional thesis work.

• Practical Learning: Opportunities to work on hands-on projects and apply analytical techniques to real-world data challenges.

• Graduate Cooperative Education: Opportunities to gain professional experience through the university's Graduate Cooperative Education Program.

• AI-Focused Education: Access to core and elective artificial intelligence courses that strengthen students' technical and analytical capabilities.

Reputation (Employability Rankings)

The Department of Mathematical Sciences at Stevens Institute of Technology offers a data science program that combines mathematical theory, statistical analysis, and advanced computing applications. The university is recognized for its technology-focused education and proximity to New York City's financial and technology industries, providing students with opportunities to connect with leading employers and professionals.

Students benefit from:

• National Recognition: Ranked No. 33 in the United States among the best master's in data science programs by Fortune in 2025.

• Industry Connections: Networking and recruitment opportunities with companies such as Google, Microsoft, and Bloomberg.

• Research Facilities: Access to the Stevens MakerCenter, Prototype and Object Fabrication Lab, and other specialized research facilities.

• Professional Experience: Graduate cooperative education, research projects, and internship opportunities.

• Career Outcomes: The School of Engineering and Science reported an average starting salary of $89,394 six months after graduation for its Class of 2024.

Graduates can pursue careers as Data Scientists, Data Analysts, Data Engineers, Machine Learning Engineers, AI Research Scientists, Statisticians, Network Engineers, Data Analytics Specialists, Business Intelligence Analysts, Quantitative Analysts, Database Administrators, and Predictive Analytics Specialists. Stevens reports that its data science graduates have been recruited by organizations including Amazon, Bank of America, Disney, Two Sigma Investments, Cityblock Health, and New York University.