MS Data Science, Analytics and Engineering (Electrical Engineering) at Arizona State University

Data scientist is consistently ranked among the top jobs in the U.S., and there's an increasing need for all engineers to make use of data science tools such as statistics, machine learning, artificial neural networks and artificial intelligence. However, the majority of engineering occupations require subject matter expertise beyond data science.

Faculty: Ira A. Fulton Schools of Engineering  |  Department: Electrical Engineering Program  |  Duration: 30 credit hours  |  Study mode: full-time  |  Intake: Fall, Spring  |  Location: Tempe, AZ, United States

What you will study

30 credit hours and a thesis, or 30 credit hours including the required capstone course (FSE 570) Required Core (9 credit hours) Students choose one from the following: DSE 501 Statistics for Data Analysts (3) EEE 554 Probability and Random Processes (3) HSE 530 Intermediate Statistics for Human Systems Engineering (3) STP 501 Theory of Statistics I: Distribution Theory 3 (3) Students choose one from the following: CSE 511 Data Processing at Scale (3) CSE 512 Distributed Database Systems (3) IFT 530 Advanced Database Management Systems (3) Students choose one from the following: CSE 572 Data Mining (3) or DSE 572 Data Mining (3) CSE 575 Statistical Machine Learning (3) EEE 549 Statistical Machine Learning: From Theory to Practice (3) IEE 520 Statistical Learning for Data Mining (3) IFT 511 Analyzing Big Data (3) MAE 551 Applied Machine Learning for Mechanical Engineers (3) STP 550 Statistical Machine Learning (3) Concentration (9 credit hours) Students choose three courses of the following: EEE 506 Digital Spectral Analysis (3) EEE 508 Digital Image and Video Processing and Compression (3) EEE 509 DSP Algorithms and Software (3) EEE 510 Multimedia Signal Processing (3) EEE 511 Artificial Neural Computation (3) EEE 515 Machine Vision and Pattern Recognition (3) EEE 516 Physics-Based Computer Vision (3) EEE 551 Information Theory (3) EEE 554 Probability and Random Processes (3) EEE 556 Detection and Estimation Theory (3) EEE 559 Wireless Networks (3) EEE 560 Mathematical Foundations of Machine Learning (3) EEE 585 Security and Privacy in Networked Systems (3) EEE 598 Deep Learning: Foundations and Applications (3) EEE 598 Optimization for Engineers (3) Electives (6 or 9 credit hours) Culminating Experience (3 or 6 credit hours) EEE 599 Thesis (6) FSE 570 Data Science Capstone (3) Additional Curriculum Information Students should consult the academic unit for a list of approved electives and concentration course requirements.

Career pathways

Electrical engineers with a background in data science can pursue opportunities in a variety of fields to manage, analyze and extract data from large data sets, including in the following industries: circuit design energy and power systems semiconductor fabrication signal processing telecommunications

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