Data Science, Analytics and Engineering (Materials Science and Engineering), MS
Data Analytics, Data Engineering, Data Science, Machine Learning, Materials Engineer, Materials Science, approved for STEM-OPT extension, materials
Learn the data science skills needed for the modern economy while enhancing your expertise in materials science and engineering. Take high-demand courses and work with your colleagues to solve client-driven data science problems.
Data scientists are consistently ranked among the top jobs in the USA, and there is an increasing need for all engineers to make use of data science tools like statistics, machine learning, artificial neural networks and artificial intelligence. Yet the majority of engineering occupations require subject matter expertise beyond data science.
The MS program in data science, analytics and engineering with a concentration in materials science and engineering provides an advanced education in high-demand data science and materials science and engineering. A focus on probability and statistics, machine learning and data engineering is complemented by materials science and engineering-specific courses to ensure breadth and depth in data science and materials science and engineering.
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.
30 credit hours and a thesis, or
30 credit hours including the required applied project course (MSE 593)
Required Core (9 credit hours) Choose one from the following: Concentration (12 credit hours) Electives (3 or 6 credit hours) Culminating Experience (3 or 6 credit hours) Additional Curriculum Information Courses selected for 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 of their plan of study is equal to 30.
STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (3)
CSE 511 Data Processing at Scale (3), CSE 512 Distributed Database Systems (3) or IFT 530 Advanced Database Management Systems (3)
CSE 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)
IFT 512 Advanced Big Data Analytics/AI (3)
MAE 551 Applied Machine Learning for Mechanical Engineers (3)
STP 550 Statistical Machine Learning (3)
MSE 593 Applied Project (3)
MSE 599 Thesis (6)
Students should consult the academic unit for a list of approved electives and concentration course requirements.
Required Core (9 credit hours)
Choose one from the following:
Concentration (12 credit hours)
Electives (3 or 6 credit hours)
Culminating Experience (3 or 6 credit hours)
Additional Curriculum Information
Courses selected for 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 of their plan of study is equal to 30.
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 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.
Applicants are required to submit:
- graduate admission application and application fee
- official transcripts
- written statement
- professional resume
- GRE scores
- proof of English proficiency
Additional Application Information
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 regardless of their current residency.
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
Applicants who have obtained a bachelor's degree from an ABET-accredited program at a U.S.-based college or university are not required to take the GRE.
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Materials science engineers with a background in data science can pursue opportunities in a variety of fields to manage and analyze data and extract knowledge from large data sets for decision making, including in the following industries:
- aircraft design
- energy systems
- product design