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UW ECE Launches New BSECE Degree Program

Master’s & Ph.D.

Graduate Data Science Option (DSO)

The Data Science Option (DSO) is designed to meet a critical educational gap that views Data Science from an Electrical & Computer Engineering (ECE) viewpoint. With the new DSO option, the ECE graduates will be equipped to tackle modern engineering challenges using large datasets, machine learning, statistical inference and visualization techniques. Building on ECE fundamentals of statistical signal processing and controls, the ECE DSO will provide students with a strong foundation in the field of data science, developing critical knowledge and skills to apply a variety of modern data analysis techniques and tools to advance and accelerate ECE research and applications.

The DSO is intended for students with little or no background in data science, computer science or coding. The option is based on a framework developed by the University of Washington eScience Institute. The eScience Institute empowers UW researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data, providing expertise to leverage data science tools, methods and best practices in their research and education.

Curriculum

Overview of ECE DSO Requirements

The requirements to receive the ECE DSO are as follows:

  1. Complete one course from each of the following areas:
    a. Statistics
    b. Machine learning
    c. Data manipulation
    d. Department-specific requirement
  2. Participate in 2 quarters of the 1-hour eScience Seminar CHEM E599
  3. Fulfill all of the standard ECE and graduate school degree requirements
    a. https://www.ece.uw.edu/academics/grad/
    b. The Departmental requirements may be fulfilled simultaneously with the Data Science Option requirements.

This new degree option in both the Master of Science program and the Doctor of Philosophy program in Electrical Engineering. The Master of Science in Electrical Engineering program requires a total of 42 credits and the Doctor of Philosophy program requires a total of 90 credits, as outlined here: https://www.ece.uw.edu/academics/grad/

Details of implementation within the current degree requirements are outlined below. In all cases, the option will be associated day-time programs offered at the Seattle campus. The delivery mechanism will be in-person classroom (in some cases using a flipped classroom), unless public health considerations require online delivery.

For both programs, the expected learning outcomes of the Data Science Option include: an understanding of the theory underlying foundational concepts in machine learning, knowledge of experimental practices in data science, practical experience working with current algorithms and large data sets, and an appreciation for ethical issues associated with design and deployment of systems that leverage machine learning. Because the requirements and learning goals are similar, students may not receive the DSO for both a UW MS and ECE Ph.D. degrees.

Degree Requirements for the Master of Science in Electrical Engineering (Data Science):

The proposed Data Science Option shares all requirements of the standard MSEE degree, but contains a total of 17-19 credits of distinct requirements which are not part of the standard MSEE. The full requirements are below, with distinct data science requirements highlighted.

Thesis Option (42 credits total)

The thesis option requires completing 42 credits, including:

  • 20 credits minimum EE 5xx numerically graded coursework
    • Excludes seminar courses
    • Excludes EE 599
  • 13 credits minimum to 17 credits maximum of required research credits
    • 4-8 credits of EE 599 numerically graded research credits
    • 9-13 credits of EE 700 required
  • 4 credits maximum of seminar credits apply to the degree
    • 1 credit EE 500 required
    • 2 credits maximum of non-EE seminars
    • 2 credits eScience Community Seminar CHEM E 599 required
  • 10 credits maximum of non-EE coursework
    • 4 credits maximum at the 300-level
    • 4 credits maximum of ENGR 601
  • At least 30 credits total required at the 5xx level or higher
  • Data Science Coursework (15-17 credits): Complete 1 course from each of the following areas (see course lists below). Credits count toward the requirements above:
    • Statistics
    • Machine Learning
    • Data Manipulation
    • Department-specific Data Science requirements
  • Thesis: A written thesis must be submitted by the student for approval by the Master’s Supervisory Committee, followed by a final oral examination.

Coursework Option

The coursework option requires completing 42 credits, including:

  • 24 credits minimum EE 5xx numerically graded courses
    • Excludes seminar courses
    • Excludes EE 599
  • 8 credits maximum of EE 599 numerically graded research credits
  • 4 credits maximum of seminar credits to apply to the degree
    • 1 credit EE 500 required
    • 2 credits maximum of non-EE seminars
    • 2 credits eScience Community Seminar CHEM E 599 required
  • 10 credits maximum of non-EE coursework
    • 4 credits maximum at the 300 level
    • 4 credits maximum of ENGR 601
  • At least 30 credits total required at the 5xx level or higher
  • Data Science Coursework (15-17 credits): Complete 1 course from each of the following areas (see course lists below). Credits count toward the requirements above:
    • Statistics
    • Machine Learning
    • Data Manipulation
    • Department-specific Data Science requirements

Degree Requirements for the Ph.D. in Electrical Engineering (Data Science):

The proposed Data Science Option shares all requirements of the standard Ph.D. degree, but contains a total of 17-19 credits of distinct requirements which are not part of the standard Ph.D. The full requirements are below, with distinct data science requirements highlighted.

Ph.D. Degree Requirements

  • 90 credits as a graduate student; at least 60 credits must be completed at the University of Washington.
  • EE 500 Seminar: 1 credit is required (4 maximum)
  • EE 599: 5 credits maximum may be applied
  • EE 800: 27 credits dissertation research over at least three quarters
  • 2 credits eScience Community Seminar CHEM E 599 required
  • Data Science Coursework (15-17 credits): Complete 1 course from each of the following areas (see course lists below). Credits count toward the requirements above:
    • Statistics
    • Machine Learning
    • Data Manipulation
    • Department-specific Data Science requirements
  • Successful completion of the Qualifying Exam, Generals Exam & Dissertation Defense
  • The dissertation should incorporate elements of data science, which may include advances in data science methods such as machine learning and/or application of data science methods to research questions in Electrical and Computer Engineering.

Course Lists for Data Science Option

Students should confirm course availability and credits on the UW time schedule.

Statistics

ECE options

Course # Course Name Credits
EE 505 Probability and Random Processes 4
EE 508 Stochastic Processes in Engineering 3

Seattle campus options

Course # Course Name Credits
STAT 509 Introduction to Mathematical Statistics 4

Machine Learning

ECE options

Course # Course Name Credits
EE 511 Introduction to Statistical Learning 4

Seattle campus options

Course # Course Name Credits
CSE 546 Machine Learning 4

Data Manipulation

ECE options

Course # Course Name Credits
EE 518 Digital Signal Processing 4

Seattle campus options

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

Department-specific requirements as related to data science

ECE options

Course # Course Name Credits
EE 512 Probability and Random Processes 4
EE 514 Information Theory I 4
EE 515 Information Theory II 4
EE 516 Computer Speech Processing 4
EE 517 Continuous Space Language Processing (will become a permanent 4 credit course)
EE 546 Advanced Topics: Convex Optimization Algorithms (will become a permanent 4 credit course)
EE 546 Advanced Topics: Game Theory 4
EE 563 Submodular Optimization 4
EE 576 Computer Vision 3
EE 578 Convex Optimization 4
EE 596 Neural networks and Deep Learning (will become a permanent 4 credit course)

Seattle campus options

Course # Course Name Credits
CSE 547/Stat 548 Machine Learning for Big Data 5

 

 

Students should contact the ECE Graduate Program Coordinator to request review and approval for any new or existing courses not on these lists.

eScience Community Seminar

In addition to the course requirements listed above, students must also participate in 2 quarters of the 1-credit eScience Community Seminar. This is an informal environment for presentations and discussions. Topics span science, methods, and technology across the mission of the eScience Institute.

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