Program Overview

Master's–Data Science Option

The Master's–Data Science Option is designed for Mechanical Engineering Master’s students to receive credentialed training in the analysis of large datasets. This option provides students with an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research. The ME DSO is designed for students with little or no background in data science, computer science, or coding.

Requirements

The requirements for the Master's degree Data Science option are as follows:

1. Courses from three out of four of the following areas:
   • Software development for data science:
     • CSE 583: Software Development for Data Scientists (4 credits)
     • ME 574: Introduction to Applied Parallel Computing for Engineers (3 credits)
   • Statistics and machine learning:
     • CSE416/STAT416: Introduction to Machine learning (4 credits)
     • ME/EE 578: Convex Optimization (4 credits)
     • ME 599: Machine Learning Control (3 credits)
     • CSE 579: Intelligent Control Through Learning and Optimization (3 credits)
     • ME 571: Data Driven Modeling of Dynamical Systems (4 credits)
     • ME 599: Introduction of AI for Clean Energy (3 credits)
     • Alternatives:
       • STAT 527: Nonparametric regression and classification (3 credits)
     • Advanced option:
       • CSE 546/STAT 535: Machine Learning (4/3 credits)
       • STAT 509: Introduction to Mathematical Statistics (4 credits)
       • STAT 512-513: Statistical Inference (4 credits)
   • Data management and data visualization:
     • CSE 414: Introduction to Database Systems (4 credits)
     • CSE 412: Introduction to Data Visualization (4 credits)
     • HCDE 411/511: Information for Visualization (4 credits)
     • BIOEN 420: Medical Imaging (4 credits)
     • BIOEN 451/551: Optical Coherence Tomography (4 credits)
     • BIOEN 546: Fundamentals of Biomedical Imaging (4 credits)
     • Advanced Option:
       • CSE 544: Principles of DBMS (4 credits)
       • CSE 512: Data Visualization (4 credits)
   • Department specific requirement:
     • CSE 455: Computer Vision (4 credits)
     • EE/CSE 576: Computer Vision (3 credits)
     • ME/EE 578: Convex Optimization (4 credits)
     • ME 599: Machine Learning Control (3 credits)
     • ME 574: Introduction to Applied Parallel Computing for Engineers (3 credits)
     • CSE 579: Intelligent Control Through Learning and Optimization (3 credits)
2. Seminar:
   • 2 quarters of the eScience Community Seminar OR ME Data Driven seminar with Professor Steve Brunton. Seminar credits do not count toward the 42 credit graduation requirements.
3. Additional Quantitative Methods Course:
   • ALL students are required to take at least one additional course in quantitative methods (statistics, applied mathematics, mathematics, or computational science).
   • Course options:
     • CSE416/STAT416: Introduction to Machine learning (4 credits)
     • STAT 527: Nonparametric Regression and Classification (3 credits)
     • STAT 535: Statistical Learning: Modeling, Prediction, and Computing (3 credits)
     • STAT 509: Econometrics I: Introduction to Mathematical Statistics (4 credits)
     • ME/EE 578: Convex Optimization (4 credits)
     • ME 599: Machine Learning Control (3 credits)
     • STAT 512/513: Statistical Inference (4 credits)
     • ME 571: Data Driven Modeling of Dynamical Systems (4 credits)

Notes

• Students may not count any course toward both the ME coursework requirements and the Data Science requirements.
• For example, if students take ME 574 and count it toward the computational or numerical analysis requirement, they cannot use this course to fulfill the Data Science requirement.
• Students must ensure that there’s a minimum of 9 distinct credits taken for the Data science option.