Carnegie Mellon University

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Program Overview

The Mellon College of Science is striving to ensure that students are better prepared for the next career step. As part of this commitment to training the next generation of scientific leaders, we have created the M.S. in Data Analytics for Science (MS-DAS) program.

The MS-DAS program is designed initially as a one-year program. Students will commence the program in the fall and take a rigorous set of courses through the spring in applied linear algebra, programming, machine learning, statistical methods and neural networks that will equip them with the necessary data analytics techniques to solve modern scientific problems. Courses will be offered through the Mellon College of Science, Department of Statistics and the Pittsburgh Supercomputing Center, a world leader in high-performance computing and data analytics.

The program culminates in a semester-long capstone project in collaboration with industry partners providing students with a concrete understanding of how to impact scientific discovery through applied data analytics skills. To ensure students are prepared for a data science career, a required 6-week mini course on communications and professional development will be completed in the Spring semester.

Students must complete a minimum of 99-units to meet the degree requirements.

Curriculum Overview 

The first semester provides foundational mathematics, statistics and programming skills necessary to understand the basics of computational modeling, analytical tools, and machine learning. Students will take a combination of 6-week mini courses and semester-long courses to provide the depth and breadth needed to move into advanced coursework in the Spring.

Mini I (August – October)

21670 - Linear Algebra for Data Science * 38610 - Modern Programming for Data Scientists

*Students may place out of this course

Mini II (October – December)

38611 - Introduction to Large-Scale Computing in Science

Full Semester (August – December)

21671 - Computational Linear Algebra
36600 - Essentials of Statistical Practice for Graduate Students - OR - 36617 - Applied Linear Models
38615 - Computational Modeling, Statistical Analysis and Machine Learning in Science


In the second semester, students will have the opportunity to apply foundational skills from the first semester and tailor the program towards their specific area of scientific interest through industry-partnered capstone and elective coursework offerings in MCS. Students will also complete a mini course in communications and professional development to prepare for career demands.

Mini III (January – March)

38613 - Communication Skills and Professional Development

Mini IV (March –May)

38612 - Information Visualization for Scientists

Full Semester (January – May)

38616 - Neural Networks and Deep Learning in Science Elective Course 38617 - MS-DAS Capstone Project Course

In the Spring semester, students are required to complete a 9 or 12-unit elective course that allows for applied specialization from the following list of approved courses:

02604 - Fundamentals of Bioinformatics 02613 - Algorithms & Advanced Data Structures 02710 - Computational Genomics 09763 - Molecular Modeling & Computational Chemistry 09860 - Digital Molecular Design Studio 10708 - Probabilistic Graphical Models 10725 - Convex Optimization 16720 - Computer Vision 21270 – Introduction to Mathematical Finance 21690 – Methods of Optimization 21765 – Intro to Parallel Computing & Scientific Computation 33456 – Advanced Computational Physics 36662 – Methods of Statistical Learning 42685 – Biostatistics

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