Data science, analytics and engineering
(MS, PhD)

Our data science, analytics and engineering degree is designed to produce data scientists, analysts, and engineers who can manage data and convert that data into relevant information for making decisions. Our PhD program will train scientists and engineers in development of new systems and algorithms for collecting, cleaning, storing, valuing, aggregating, fusing, summarizing, managing and drawing inferences from high dimension, high volume, heterogeneous data streams for knowledge discovery.

This degree program is a collaboration between the School of Computing and Augmented Intelligence (SCAI) and the School of Mathematical and Statistical Sciences (SoMSS). Our faculty are internationally recognized for their research, conducting many funded, state-of-the-art research projects for both government and industry. They are not only involved in leading-edge research from both methodological and applications perspectives, but they also actively advise students, teach innovation and continuously improve their curriculum.

Data Science, Analytics and Engineering (Computing and Decision Analytics), MS: This concentration in the MS program in data science, analytics and engineering provides an advanced education in high-demand data science and computing and industrial engineering. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by computing and industrial engineering-specific courses to ensure breadth and depth in data science and computing and industrial engineering.

Data Science, Analytics and Engineering, PhD: The PhD program in data science, analytics and engineering engages students in fundamental and applied research as preparation for careers in academia, government or industry. 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 for data analytics and engineering.

The doctoral program provides a rigorous education with research and educational experiences that allow students to pursue careers in advanced research, teaching or state-of-the-art practice. Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area of data science, analytics and engineering.

PhD Degree requirements

All students must take qualifying exams covering the required core courses within one year of matriculation into the program. 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 will need to take a different elective course to reach the total number of credit hours required for the program. Other coursework may be used with approval of the academic unit to fulfill these requirements.


Please see the ASU Graduate Policies and Procedures for additional information.

PhD Catalog Program

Deficiency course syllabi
Required core (12 credit hours)
  • IEE 520 Statistical Learning for Data Mining (3) or CSE 572 Data Mining (3)
  • IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3)
  • CSE 510 Database Management System Implementation (3)
  • CSE 543 Information Assurance and Security (3)
Other requirements (9 credit hours)

Data Engineering requirements

  • CSE 512 Distributed Database Systems (3)
  • CSE 515 Multimedia and Web Databases (3)
  • CSE 546 Cloud Computing (3)

Data Analytics requirements

  • CSE 575 Statistical Machine Learning (3) or ECE 598 Topic: Statistical Machine Learning (3)
  • CSE 578 Data Visualization (3)
  • One of IEE 578 Regression Analysis (3), IEE 620 Optimization I (3), APM 523 Optimization (3) or EEE 598 Topic: Convex Optimization (3)
Elective courses (39 credit hours)

CSE 511 Data Processing at Scale
CSE 512 Distributed Data Systems
CSE 515 Multimedia and Web Databases
CSE 546 Cloud Computing
CSE 548 Advanced Computer Network Security
CSE 550 Combinatorial Algorithms and Intractability
CSE 551 Foundations of Algorithms
CSE 552 Randomized and Approximation Algorithms
CSE 555 Theory of Computation
CSE 556 Game Theory with Applications to Networks
CSE 561 Modeling and Simulation Theory and Application
CSE 565 Software Verification, Validation and Testing
CSE 569 Fundamentals of Statistical Learning and Pattern Recognition
CSE 571 Artificial Intelligence
CSE 573 Semantic Web Mining
CSE 574 Planning and Learning Methods in AI
CSE 575 Statistical Machine Learning
CSE 576 Topics in Natural Language Processing
CSE 578 Data Visualization
CSE 579 Knowledge Representation and Reasoning
CSE 598 Algorithms in Computational Biology
IEE 506 Web-Enabled Decision Support Systems
IEE 511 Analysis of Decision Processes
IEE 512 Introduction to Financial Engineering
IEE 521 Urban Operations Research
IEE 526 Operations Research in Healthcare
IEE 545 Advanced Simulating Stochastic Systems
IEE 570 Advanced Quality Control
IEE 572 Design Engineering Experiments
IEE 573 Reliability Engineering
IEE 574 Applied Deterministic Operations Research
IEE 575 Applied Stochastic Operations Research Models
IEE 577 Data Science for Systems Informatics
IEE 578 Regression Analysis
IEE 579 Time Series Analysis/Forecasting
IEE 582 Response Surfaces/Process Optimization
IEE 605 Foundations of Information Systems Engineering
IEE 620 Optimization I
IEE 622 Optimization II
IEE 640 Probability and Stochastic Processes
IEE 672 Adv Topics-Experimental Design
STP 505 Bayesian Statistics
STP 526 Theory of Statistical Linear Models
STP 530 Applied Regression Analysis
STP 532 Applied Nonparametric Statistics
STP 533 Applied Multivariate Analysis
STP 540 Computational Statistics
STP 598 Topic: Causal Inference
STP 598 Topic: Machine Learning / Statistical Learning
STP 598 Topic: Time Series
STP 598 Topic: Advanced Design of Experiment
APM 505 Applied Linear Algebra
APM 523 Optimization
APM 525 High-Performance Computing
APM 598 Topic: Fourier Analysis and Wavelets
EEE 551 Information Theory
EEE 558 Wireless Communications
EEE 581 Filtering of Stochastic Processes
EEE 585 Security and Privacy in Networked Systems
EEE 591 Topic: Machine Learning and Data Science: Theory to Practice
EEE 598 Topic: Statistical Machine Learning from Foundations to Algorithm
EEE 598 Topic: Special Topics in Machine Learning
EEE 598 Topic: Distributed and Large Scale Optimization
EEE 598 Topic: Remote Sensing and Adaptive Radar
EEE 598 Topic: Introduction to Complex Networks and Machine Learning
EEE 598 Topic: Speech and Audio Processing and Perception
EEE 598 Topic: Machine Learning for Smart Grid
EEE 598 Topic: Neuromorphic Hardware Design

Research (12 credit hours)

DSE 792 Research (12)

Culminating experience (12 credit hours)

DSE 799 Dissertation (12)

Data science, analytics and engineering, PhD: Both a written and an oral comprehensive examination are required on or before completion of the 57 credit hours of course work in the program of study. The candidate must also successfully pass the dissertation prospectus and the dissertation oral defense.

Please see the ASU Graduate Policies and Procedures for additional information.

Graduate admissions

Thank you for your interest in pursuing a graduate degree in the School of Computing and Augmented Intelligence. Here you can learn more about the admissions process and application requirements.

Deadline dates

Program Fall semester Spring semester
Data Science, Analytics and Engineering January 15 (PhD) September 15 (PhD)



Admission requirements

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 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 applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = “A”) in an applicable master’s degree program.

For questions regarding supplemental materials, such as letters of recommendation requirements or your statement of purpose, contact [email protected]. For questions regarding the online graduate application or the status of documents, please contact Graduate Admissions.

Application requirements

Begin by visiting the Graduate Admissions website and completing the Graduate Admissions application and paying the application fee. The fee is nonrefundable and the application will not be processed until the fee is received.

In addition to completing the ASU Graduate Admissions application, the following materials must also be submitted to complete your application package:*

U.S. Residents:

  • One set of official transcripts from every college and university attended, including ASU, unless the student graduated from ASU
  • Official GRE test scores.** Click here for the average GRE scores for applicants. The ASU institution code is: 4007. If a department code is required use: 000.
  • Three letters of recommendation
  • Statement of purpose

International Applicants:

  • Academic credentials (all international records must be submitted in the original language accompanied by an official English translation). If you have attended a U.S. institution, one set of official transcripts from every college and university attended, except ASU.
  • Official GRE General test scores taken within the last five years.** Click here for the average GRE scores for applicants. The ASU institution code is: 4007. If a department code is required use: 000.
  • Official TOEFL score, taken within the last two years (please see ASU’s English Proficiency requirements). The TOEFL score must be valid on the first day of class for the term the student is applying for. SCAI requires that TOEFL scores must be above 575 (paper), or 90 (iBT). We also accept IELTS with a minimum overall band scores of 7.0 or the Pearson Test of English (PTE) with a minimum score of 65 or higher. Please see the Graduate Admissions website for TOEFL deadline dates. The ASU institution code is: 4007. If a department code is required use: 000.
  • Three letters of recommendation
  • Statement of purpose

The required materials should be mailed to:

If sending by stamped mail:
Admission Services Applicant Processing
Arizona State University
PO Box 871004
Tempe, AZ 85287-1004
If sending by FedEx, DHL or UPS:
Arizona State University
Admission Services Applicant Processing
1150 East University Drive Building C, Room 226
Tempe, AZ 85281

Please include the document reference number on all materials sent. Applications are not evaluated until all required documents have been received.