Program Overview

Master of Science in Data Analytics for Science

The Master of Science in Data Analytics for Science (MS-DAS) degree program at Carnegie Mellon University prepares students to meet the rising demand for data analytics in scientific domains. This program is tailored for students from biology, physics, math, chemistry, or related fields, and provides leading-edge training in data analytics, computational modeling, data visualization tools, and machine learning.

Key Features of the MS-DAS Degree Program

• Pittsburgh Supercomputing Center: Train with leading experts and technologies at one of the nation’s top centers for data analytics.
• Capstone Project: Tackle a real data analysis challenge in a semester-long capstone with industry partners.
• Professional Development: Complete a six-week course on communication and professional development for data science careers.

Rigorous, Career-Focused Curriculum

The MS-DAS program is a one-year degree program that offers a unique foundation in modern programming, computational modeling, parallel computing, and statistical analysis. Coursework covers data analysis for science topics such as linear algebra, statistical modeling, scientific machine learning, computational modeling, high-performance computing, and professional communication. While Python is the primary programming language of instruction in the MS-DAS program, students will be introduced to other programming languages, such as SQL for data querying and R for statistical analysis and visualization.

Pittsburgh Supercomputing Center

MS-DAS students benefit from access to the Pittsburgh Supercomputing Center and have opportunities to work with these powerful technologies and domain experts to graduate with truly unique, advanced experience. PSC provides access to several of the most powerful systems for high-performance computing, communications, and data storage available to scientists and engineers nationwide for unclassified research.

Hands-On Learning

The semester-long capstone course allows students to work directly with industry partners on real-world, data-driven challenges. Drawing from a range of sectors, these partnerships connect students with professionals tackling complex scientific problems. Students and their fellow students will work in teams, guided by expert faculty, to apply advanced data analytics techniques to develop meaningful solutions. Regular check-ins with partners provide feedback on their approach while strengthening their communication and presentation skills. The capstone is a valuable opportunity to grow their network and connect with potential employers in science and data-focused fields.

Career Opportunities in Data Analytics

MS-DAS students have opportunities to interact with corporate partners through the capstone project and various networking events throughout the academic year. The physical and life sciences industries need graduates with both a science foundation and knowledge of machine learning and AI. These tools are necessary for scientific innovation. The MS-DAS program uniquely positions students for success in fields such as therapeutics, pharmaceuticals, medical device technology, financial services, and autonomous transportation, to name a few.

MS in Data Analytics for Science Program Admission and Financial Aid

The MS-DAS program welcomes applicants who do not have a background in statistical practices, mathematical probability, or advanced calculus. The program is a one-year degree program, and tuition fees are not specified in the provided context. However, it is mentioned that Carnegie Mellon’s Career and Professional Development Center is available to discuss career plans and assist with all aspects of the job search process.

Application Deadline

The application deadline for the MS-DAS program is January 15, 2026.

Related Programs

• M.S. in Computational Biology
• Ph.D. in Astronomy and Astrophysics

Why Choose the MS in Data Analytics for Science Program at Carnegie Mellon

As society becomes increasingly data-driven, the ability to apply data analytics to scientific domains is not just advantageous — it’s essential. The MS-DAS program at Carnegie Mellon University prepares students to meet this rising demand, with leading-edge training in data analytics, computational modeling, data visualization tools, and machine learning. Unlike other master’s degrees in data science designed for computer science or engineering backgrounds, the MS-DAS program is tailored for students from biology, physics, math, chemistry, or related fields. Students build on their scientific expertise while mastering machine learning and advanced computational tools for discovery, innovation, and impact in their fields.