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, which enhances enterprise performance as well as scientific investigation.
The PhD 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 perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed. This is accomplished through a rigorous education with research and educational experiences.
Students complete a foundational core covering database management, information assurance, statistical learning and statistical theory before choosing to focus on 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 36 months. This OPT work authorization term 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 the degree through ASU Online.
- College/school:
Ira A. Fulton Schools of Engineering
- Location: Tempe
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 take a data engineering or data analytics course and have it meet an elective requirement at the same time. Students need to 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 research hours in excess of 12 will add these credit hours to their electives and additional research. Electives include: When approved by the student's supervisory committee and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to 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 IEE 520 Statistical Learning for Data Mining (3) or EEE 549 Statistical Machine Learning: From Theory to Practice (3)
IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (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 matriculation into the program.
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 provide proof of English proficiency regardless of their current residency.
ASU does not accept the GRE® General Test at home edition.
If the student is assigned any deficiency coursework upon admission, those classes must be completed with a grade of "B" (scale is 4.00 = "A") or higher within two semesters of admission to the program. Deficiency courses do not apply to the total credit hours required to complete the degree program.
Deficiency courses are:
CSE 205 Object-oriented Programming and Data Structures
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 has the following program outcomes:
- 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 accomplishment at advancing the state of the art in their chosen area, enabling them to pursue careers in the following fields:
- advanced research
- business
- government
- industry
- teaching
Computer Science and Engineering Program
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CTRPT 105
SCAI.Grad.Admission@asu.edu
480-965-3199
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.
