Graduate Programs
About our programs
The Data Science, Analytics and Engineering graduate programs are a set of interdisciplinary master’s degrees that are housed in different schools within the Ira A. Fulton Schools of Engineering. Students should apply to the concentration that best fits their background in order to ensure proper academic preparation and success.
They say data is the new oil; the ASU DSAE graduate program will enable our students to extract, process, and manage this new resource and gain a wealth of knowledge in data science.
Rong PanGraduate Program Chair
Data Science, Analytics and Engineering (Computing and Decision Analytics)
This concentration focuses on computing and industrial engineering.
Data Science, Analytics and Engineering (Electrical Engineering)
This concentration focuses on electrical engineering.
Data Science, Analytics and Engineering (Materials Science and Engineering)
This concentration focuses on material science and engineering.
Data Science, Analytics and Engineering (Sustainable Engineering and Built Environment)
This concentration focuses on sustainable engineering and the built environment.
Data Science, Analytics and Engineering (Bayesian Machine Learning)
This concentration focuses on bayesian learning, decision-making, and computation.
Data Science, Analytics and Engineering (Computational Models and Data)
This concentration focuses on computational skills in optimization, machine learning, stochastic processes and dynamical systems.
Data Science, Analytics and Engineering (Human Centered Applications)
Launching in Spring 2024!
This concentration focuses on the human experience and needs with machine learning and AI applications.
Data Science, Analytics and Engineering (Mechanical and Aerospace Engineering)
This concentration focuses on how data science interacts with concepts in mechanical and aerospace engineering, such as controls, energy systems, aeronautics, and mechanics.
Ready to apply? Submit your application on the graduate admissions website.
Graduate admissions information
Applicants should apply to the program that best matches their undergraduate degree in order to ensure proper academic preparation for their program of study. Refer to the information below for the admission requirements.
Application deadlines
Fall semester:
Preference is given to complete School of Electrical, Computer and Energy Engineering graduate applications received by December 31. Admission results should be available by March 1. Applications received after this preferred deadline will be considered.
Spring semester:
Preference is given to complete School of Electrical, Computer and Energy Engineering graduate applications received by July 31. Admission results should be available by October 1. Applications received after this preferred deadline will be considered.
Eligibility and GPA Requirements
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 they must have a minimum cumulative GPA of 3.00 (scale is 4.00 = “A”) in an applicable master’s degree program.
An applicant whose native language is not English must demonstrate proficiency in the English language by scoring at least 90 on the TOEFL iBT, 7 on the IELTS or 115 on the Duolingo English test.
Applicants are no longer required to submit Graduate Record Examinations, or GRE, scores as of fall 2023.
Application requirements
Applicants are required to submit:
- graduate admission application and application fee
- official transcripts
- written statement
- professional resume
- proof of English proficiency
Recommended academic preparation
The Data Science, Analytics and Engineering program is a multidisciplinary program that requires students to take classes outside of their school. It is recommended that all applicants have the following background information prior to applying.
All applicants must demonstrate relevant coursework or experience in the following three areas:
- 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)
- Undergraduate linear algebra (e.g., MAT 242 Elementary Linear Algebra)
- Familiarity with Matlab, Python, SQL, R, or other relevant programming skills (in the professional resume)
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 IT Support Certificate
- Google Data Analytics Certificate