Data Science, Analytics and Engineering (Computing and Decision Analytics), MS
Analytics, Data Analysis, Data Driven Decision Making, Data Science, Data analysis and mining, approved for STEM-OPT extension, computing, statistics
Learn the data science skills needed for the modern economy while enhancing your expertise in computing and industrial engineering. You can 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.
This concentration in the Master of Science 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.
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
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.
30 credit hours and a thesis, or
30 credit hours including the required capstone course (FSE 570)
Required Core (9 credit hours) DSE 501 Statistics for Data Analysts (3), EEE 554 Probability and Random Processes (3), HSE 530 Intermediate Statistics for Human Systems Engineering (3) or STP 501 Theory of Statistics I: Distribution Theory 3 (3) CSE 511 Data Processing at Scale (3) or CSE 512 Distributed Database Systems (3) or IFT 530 Advanced Database Management Systems (3) 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 for 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.
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)
MAE 551 Applied Machine Learning for Mechanical Engineers (3)
STP 550 Statistical Machine Learning (3)
Complete one course for each of the following areas:
data analysis (3)
data assurance and security (3)
machine learning (3)
optimization (3)
CSE, IEE or SER 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 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
- 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.
Before applying to the MS program, students are required to have completed three semesters or nine credit hours of Calculus I, II and III and recommended to have completed a discrete mathematics course.
Students assigned any additional admission requirements upon admission must complete those classes with a grade of "B" (scale is 4.00 = "A") or higher within two semesters of admission to 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
The applicant's undergraduate GPA and depth of preparation in computer science and engineering are the primary factors affecting admission.
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 12/31 | Priority |
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 07/31 | Priority |
Computing and industrial 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:
- consulting
- data and analytics management
- data engineering
- information systems
- manufacturing
Computer Science and Engineering Program
|
CTRPT 105
scai.grad.admission@asu.edu
480-965-3199
Admission deadlines
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.
