Data Science, Analytics and Engineering, PhD
Analytics, Big Data, Data Engineering, Data Science, approved for STEM-OPT extension, computing, statistics
Learn to meet the need for data-driven discovery of new knowledge and decision-making. You'll be prepared to enhance enterprise performance and scientific investigation.
The Doctor of Philosophy program in data science, analytics and engineering engages students in fundamental and applied research.
The program's educational objective is to develop each student's ability to conduct original research in the design and application of data-driven methods to address major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed --- accomplished through a rigorous education that integrates research and learning experiences.
Students complete a foundational core that covers database management, information assurance, statistical learning and statistical theory before focusing on their choice of data analytics or data engineering. The program culminates in the production of a dissertation.
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
84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation
Required Core (12 credit hours) Electives and Additional Research (39 credit hours) Research (12 credit hours) Other Requirements (9 credit hours) Culminating Experience (12 credit hours) Additional Curriculum Information The dissertation prospectus should be submitted and its oral defense completed no later than one year following completion of the 60th credit hour and also no later than the fourth year in the program. Students must select coursework from either the data engineering or the data analytics requirements. Students should see the academic unit for the approved course list. Students cannot use a data engineering or data analytics course to meet an elective requirement at the same time; they must take a different elective course to reach the number of credit hours required for the program. Other coursework may be used with the approval of the academic unit to fulfill these requirements. Twelve credit hours of DSE 792 Research are required, and up to 24 credit hours are allowed on the plan of study. Students with more than 12 research credit hours will apply the excess credit hours to their electives and additional research. Electives include: When approved by the student's supervisory committee and the Graduate College, 30 credit hours from a previously awarded master's degree can be used for this degree. If students do not have a previously awarded master's degree, the 30 hours of coursework are to be made up of electives to reach the required 84 credit hours.
CSE 511 Data Processing at Scale (3)
CSE 543 Information Assurance and Security (3)
CSE 572 Data Mining (3) or EEE 549 Statistical Machine Learning: From Theory to Practice (3) or IEE 520 Statistical Learning for Data Mining (3)
EEE 554 Probability and Random Processes (3) or IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3)
DSE 792 Research (12)
data engineering coursework or
data analytics coursework
DSE 799 Dissertation (12)
All students must take qualifying exams covering the required core courses within one year of entering the program.
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 engineering, computer science, mathematics, statistics 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
- two letters of recommendation
- letter of intent or written statement
- GRE scores
- proof of English proficiency
Additional Application Information
An applicant whose native language is not English must demonstrate proficiency in the English language with a TOEFL iBT® score of 4.5, or 90 (taken at at testing center) if taken before January 21, 2026; 7 on the IELTS; or 115 on the Duolingo English test, regardless of current residency.
Before they apply to the program, students must have completed two semesters or six credit hours of calculus, equivalent to Calculus I and II, with a grade of "C" (scale is 4.00 = "A") or higher. It is also recommended that students complete a discrete math course before admission.
ASU does not accept the GRE® General Test at home edition.
Students assigned any additional courses upon admission must complete them with a grade of "C" (scale is 4.00 = "A") or higher within two semesters of entering the program. Additional admission requirements courses include:
CSE 310 Data Structures and Algorithms
IEE 380 Probability and Statistics for Engineering Problem Solving
MAT 242 Elementary Linear Algebra or MAT 342 Linear Algebra or MAT 343 Applied Linear Algebra
MAT 267 Calculus for Engineers III
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 01/15 | Priority |
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 09/15 | Priority |
Program learning outcomes identify what a student will learn or be able to do upon completion of their program. This program’s learning outcomes include the following:
- Apply the tools and methods from industrial statistics, operations research, machine learning, computer science and computer engineering on solving data analytic problems.
- Manage large, heterogeneous data sets for knowledge discovery.
- Conduct research resulting in an original contribution to knowledge in data sciences.
Graduates demonstrate proficiency with existing methodology and significant achievement in advancing the state of the art in their chosen area, preparing them for careers in the following fields:
- advanced research
- business
- government
- industry
- teaching
Computer Science and Engineering Program
|
CTRPT 105
SCAI.Grad.Admission@asu.edu
480-965-3199
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. 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.
