Data Science, Analytics and Engineering (Sustainable Engineering and Built Environment), MS
Civil Engineering, Construction Management, Data Analytics, Data Engineering, Data Science, Machine Learning, Transportation, approved for STEM-OPT extension, environmental engineering
Learn the data science skills needed for the modern economy while enhancing your expertise in sustainable engineering and the built environment. You'll take high-demand courses and work with 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. Yet 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 sustainable engineering and the built environment provides an advanced education in this high-demand area. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by sustainable engineering and built environment-specific courses to ensure breadth and depth in data science and sustainable engineering and the built environment.
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
30 credit hours and a thesis, or
30 credit hours including the required capstone course (FSE 570)
Required Core (9 credit hours) Students choose one: Students choose one: Concentration (9 or 12 credit hours) Electives (6 or 9 credit hours) Culminating Experience (3 or 6 credit hours) Additional Curriculum Information Courses selected for the 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 in their plan of study is equal to 30.
Students choose one:
DSE 501 Statistics for Data Analysts (3)
EEE 554 Probability and Random Processes (3)
HSE 530 Intermediate Statistics for Human Systems Engineering (3)
STP 501 Theory of Statistics I: Distribution Theory 3 (3)
CSE 511 Data Processing at Scale (3)
CSE 512 Distributed Database Systems (3)
IFT 530 Advanced Database Management Systems (3)
CSE 572 Data Mining (3) or DSE 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)
CEE 501 Artificial Intelligence for Civil Engineers (3)
two or three additional courses from an approved list
CEE/CON/EVE 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 must 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 current residency by scoring at least 4 on the Internet-based TOEFL (iBT) or a score of 80 if taken before January 21, 2026, in a testing center, 6.5 on the IELTS or 105 on the Duolingo English test.
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, or 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 their 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 |
Civil, environmental, sustainable or construction management engineers with a background in data science can pursue opportunities in a variety of fields. They manage and analyze data and extract knowledge from large data sets for decision-making in industries that include:
- building
- construction
- environmental remediation
- transportation
- water treatment
School of Sustainable Engineering & Built Envirnmt
|
HLMK 437
sebe.advising@asu.edu
480-965-0595
Program term definitions
Accelerated programs allow students the opportunity to expedite the completion of their degree.
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.
Concurrent degrees allow students to pursue their own personal or professional interests, earn two distinct degrees and receive two diplomas. To add a concurrent degree to your existing degree, work with your academic advisor.
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