Data Science, Analytics and Engineering (Electrical Engineering), MS
Big Data, Data Analytics, Data Engineering, Data Science, Electrical Engineering, Machine Learning, approved for STEM-OPT extension
Learn the data science skills needed for the modern economy while enhancing your expertise in electrical engineering in this distinct master's degree program. You'll take high-demand courses and work with your colleagues to solve client-driven data science problems.
Data scientist is consistently ranked among the top jobs in the U.S., and there is 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.
The Master of Science program in data science, analytics and engineering with a concentration in electrical engineering provides an advanced education in high-demand data science and electrical engineering. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by electrical engineering-specific courses to ensure breadth and depth in data science and electrical engineering.
This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization period may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.
The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.
- College/school:
Ira A. Fulton Schools of Engineering
- Location: Tempe
- STEM-OPT extension eligible: Yes
Acceptance to the graduate program requires a separate application. Students typically receive approval to pursue the accelerated master’s during the junior year of their bachelor's degree program. Interested students can learn about eligibility requirements and how to apply.
30 credit hours and a thesis, or
30 credit hours including the required capstone course (FSE 570)
Required Core (9 credit hours) DSE 501 Statistics for Data Analysts (3), EEE 554 Probability and Random Processes (3), HSE 530 Intermediate Statistics for Human Systems Engineering (3) or STP 501 Theory of Statistics I: Distribution Theory 3 (3) CSE 511 Data Processing at Scale (3), CSE 512 Distributed Database Systems (3) or IFT 530 Advanced Database Management Systems (3) Choose one from the following: Concentration (9 credit hours) EEE506 Digital Spectral Analysis (3) EEE515 Machine Vision and Pattern Recognition (3) EEE 554 Probability and Random Processes (3) EEE556 Detection and Estimation Theory (3) Electives (6 or 9 credit hours) Culminating Experience (3 or 6 credit hours) Additional Curriculum Information Courses selected for required core or concentration may not be used as elective coursework on the same plan of study. Students should check with their academic advisor to ensure that the total number of credit hours of their plan of study is equal to 30.
CSE 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)
Choose three courses of the following:
EEE508 Digital Image and Video Processing and Compression (3)
EEE509 DSP Algorithms and Software (3)
EEE510 Multimedia Signal Processing (3)
EEE 511 Artificial Neural Computation (3)
EEE516 Physics-Based Computer Vision (3)
EEE 551 Information Theory (3)
EEE559 Wireless Networks (3)
EEE 560 Mathematical Foundations of Machine Learning (3)
EEE585 Security and Privacy in Networked Systems (3)
EEE598 Deep Learning: Foundations and Applications (3)
EEE598 Optimization for Engineers (3)
EEE 599 Thesis (6)
FSE 570 Data Science Capstone (3)
Students should consult the academic unit for a list of approved electives and concentration course requirements.
General university admission requirements:
All students are required to meet general
university admission requirements.
U.S. applicants | International applicants | English proficiency
Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.
Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in computing, engineering, mathematics, statistics, operations research, information technology or a related field from a regionally accredited institution.
Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.
Applicants are required to submit:
- graduate admission application and application fee
- official transcripts
- written statement
- professional resume
- proof of English proficiency
Additional Application Information
An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency. Applicants demonstrate proficiency in the English language by scoring at least 90 on the TOEFL iBT (taken in a testing center), 7 on the IELTS or 115 on the Duolingo English test.
All applicants must demonstrate relevant coursework or experience in the following three areas:
- familiarity with MATLAB, Python, SQL, R, or other relevant programming skills (in the professional resume)
- undergraduate linear algebra (e.g., MAT 242 Elementary Linear Algebra)
- undergraduate statistics or probability (e.g., IEE 380 Probability and Statistics for Engineering Problem Solving, STP 420 Introductory Applied Statistics, STP 421 Probability, EEE 350 Random Signal Analysis)
In addition, applicants without an undergraduate degree in computer science, computer engineering, software engineering, information technology, industrial engineering, operations research, statistics or a related computing field must show evidence (in the professional resume) of at least one of the following certifications or equivalent experience:
- AWS-certified cloud practitioner
- Google data analytics certificate
- Google IT support certificate
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 12/31 | Priority |
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 07/31 | Priority |
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
Electrical Engineering Program
|
GWC 209
AskEE@asu.edu
480-965-3424
3 year programs
These programs allow students to fast-track their studies after admission and earn a bachelor's degree in three years or fewer while participating in the same high-quality educational experience of a 4-year option. Students should talk to their academic advisor to get started.
Accelerated master's
These programs allow students to accelerate their studies to earn a bachelor's plus a master's degree in as few as five years (for some programs).
Each program has requirements students must meet to be eligible for consideration. Acceptance to the graduate program requires a separate application. Students typically receive approval to pursue the accelerated master’s during the junior year of their bachelor's degree program. Interested students can learn about eligibility requirements and how to apply.
