Data Science, Analytics and Engineering, MS
Analytics, Data Analysis, Data Science, Data analysis and mining, statistics
ASU is not currently accepting applications for this program. Applicants interested in the MS program in data science, analytics and engineering should apply directly to one of the concentrations: computing and decision analytics, electrical engineering, materials science and engineering, or sustainable engineering and built environment.
Learn the data science skills needed for the modern economy in this unique master's degree program while enhancing your expertise in your chosen engineering or mathematics field. 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, artificial intelligence and data mining. Yet, the majority of engineering occupations require subject matter expertise beyond data science.
The MS program in data science, analytics and engineering enables students to receive an advanced education in high-demand data science and an engineering field in an integrated program. A core curriculum in probability and statistics, machine learning, and data engineering is complemented by concentration-specific courses to ensure breadth and depth in data science and a core engineering discipline.
- College/school:
Ira A. Fulton Schools of Engineering
30 credit hours and a thesis, or
30 credit hours including the required capstone course (FSE 570)
Required Core (9 credit hours) Choose one from the following: Elective (15 or 18 credit hours) Culminating Experience (3 or 6 credit hours) Additional Curriculum Information
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)
FSE 570 Data Science Capstone (3)
CSE, IEE or SER 599 Thesis (6)
Students should consult the academic unit for a list of approved electives.
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
- letter of intent
- 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.
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 technical tools including emerging concepts in machine learning and data science to the analysis of large heterogeneous data sets.
- Create solutions to real engineering problems.
- Apply concepts in machine learning and data science to applications in their selected concentration.
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
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CTRPT 105
PSNoMail@asu.edu
480-555-3199
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.