Skip to main content

Students

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
  • Master of Science in Engineering (Mechanical Engr: Data Sci): For students with a non-ME academic background

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 Name Credits
CSE 583 Software Development for Data Scientists 4
AMATH 583 High Performance Scientific Computing 5
ME 574 Introduction to Applied Parallel Computing for Engineers 3

2. Statistics and machine learning

Highly recommend

Course # Course Name Credits
CSE416/STAT416 Introduction to Machine learning 4
AMATH 582 Computational Methods for Data Analysis 5
AMATH 563 Inferring Structure of Complex Systems 5
AMATH 515 Fundamentals of Optimization 5
ME/EE 578 Convex Optimization 4
ME 599 Machine Learning Control 3
CSE 599U Reinforcement Learning 4
ME 599 Data-Driven Modeling of Dyanmical Systems (Manohar) 3

Alternatives

Course # Course Name Credits
STAT 527 Nonparametric regression and classification 3

Advanced option

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

Course # Course Name Credits
CSE 546/STAT 535 Machine Learning 4/3
STAT 509 Introduction to Mathematical Statistics 4
STAT 512-513 Statistical Inference 4

3. Data management and data visualization

Highly recommended

Course # Course Name Credits
CSE 414 Introduction to Database Systems 4
CSE 412 Introduction to Data Visualization 4
HCDE 411/511 Information for Visualization 4
BIOEN 420 Medical Imaging 4
BIOEN 451/551 Optical Coherence Tomography 4
BIOEN 546 Fundamentals of Biomedical Imaging 4

Advanced Option

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

Course # Course Name Credits
CSE 544 Principles of DBMS 4
CSE 512 Data Visualization 4

4. Department specific requirement

If listed above, then course doesn’t count twice

Course # Course Name Credits
CSE 455 Computer Vision 4
EE/CSE 576 Computer Vision 3
ME/EE 578 Convex Optimization 4
ME 599 Machine Learning Control 3
ME 574 Introduction to Applied Parallel Computing for Engineers 3

II. Seminar

  • 2 quarters of the eScience Community Seminar OR ME Data Drive seminar with Professor Steve Brunton. Seminar credits do not count towards 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) OR in a methodology directly relevant to their area of focus. Such courses are to be specified in each student’s Individualized Training Plan. Some classes in the Data Science Option will meet this course requirement, including (though not limited to STAT 416 (4cr), 527 (3cr), 535 (3cr), 509 (4cr), 512 (4cr), 513 (4cr), AMATH 515 (5cr), 563 (5cr), 582 (5cr), 583 (5cr), etc.)

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.