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Master's–Data Science Option

Mechanical Engineering Master’s students will receive credentialed training in the analysis of large datasets. The Data Science option provides students 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.

  • Master of Science in Mechanical Engineering (Data Science): For students with an undergraduate degree in ME or a closely related field

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

I. Courses from three out of four of the following areas

1. Software development for data science

Highly recommended

Course #Course NameCredits
CSE 583Software Development for Data Scientists4
ME 574Introduction to Applied Parallel Computing for Engineers3

2. Statistics and machine learning

Highly recommend

Course #Course NameCredits
CSE416/STAT416Introduction to Machine learning4
ME/EE 578Convex Optimization4
ME 599Machine Learning Control3
CSE 579Intelligent Control Through Learning and Optimization3
ME 571Data Driven Modeling of Dynamical Systems 4
ME 599Introduction of AI for Clean Energy3

Alternatives

Course #Course NameCredits
STAT 527Nonparametric regression and classification3

Advanced option

The following courses also serve for the “Advanced Data Science Option”

Course #Course NameCredits
CSE 546/STAT 535Machine Learning4/3
STAT 509Introduction to Mathematical Statistics4
STAT 512-513Statistical Inference4

3. Data management and data visualization

Highly recommended

Course #Course NameCredits
CSE 414Introduction to Database Systems4
CSE 412Introduction to Data Visualization4
HCDE 411/511Information for Visualization4
BIOEN 420Medical Imaging4
BIOEN 451/551Optical Coherence Tomography4
BIOEN 546Fundamentals of Biomedical Imaging4

Advanced Option

The following courses also serve for the “Advanced Data Science Option”

Course #Course NameCredits
CSE 544Principles of DBMS4
CSE 512Data Visualization4

4. Department specific requirement

If listed above, then course doesn’t count twice

Course #Course NameCredits
CSE 455Computer Vision4
EE/CSE 576Computer Vision3
ME/EE 578Convex Optimization4
ME 599Machine Learning Control3
ME 574Introduction to Applied Parallel Computing for Engineers3
CSE 579Intelligent Control Through Learning and Optimization3

II. 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. 

III. Additional Quantitative Methods Course

In addition, ALL students are required to take at least one additional course in quantitative methods (statistics, applied mathematics, mathematics, or computational science). 

Course #Course NameCredits
CSE416/STAT416Introduction to Machine learning4
STAT 527Nonparametric Regression and Classification3
STAT 535Statistical Learning: Modeling, Prediction, and Computing3
STAT 509Econometrics I: Introduction to Mathematical Statistics4
ME/EE 578Convex Optimization4
ME 599Machine Learning Control3
STAT 512/513Statistical Inference4
ME 571Data-Driven Modeling of Dynamical Systems4

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.