MS Data Science, Analytics and Engineering (Bayesian Machine Learning) at Arizona State University

Programme overview

This concentration in Bayesian machine learning within the Master of Science program in data science, analytics and engineering is offered in partnership with the School of Mathematical and Statistical Sciences.

What you will study

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: 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) Students choose one: CSE 511 Data Processing at Scale (3) CSE 512 Distributed Database Systems (3) IFT 530 Advanced Database Management Systems (3) Students choose one: 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) Concentration (9 credit hours) STP 502 Theory of Statistics II: Inference (3) STP 505 Bayesian Statistics (3) STP 540 Computational Statistics (3) STP 551 Time Series Analysis (3) Electives (6 or 9 credit hours) Culminating Experience (3 or 6 credit hours) FSE 570 Data Science Capstone (3) STP 599 Thesis (6) Additional Curriculum Information For concentration coursework, students select three courses from the list.

Career pathways

Statistician and data scientist are consistently ranked among the top jobs in the U.S. Applied statisticians with a strong background in Bayesian learning and decision can pursue opportunities in a variety of fields to collect and curate large and complex data sets related to business plans, model and communicate findings, and support rational decision-making.

Official programme page

Application information

Entry 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 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 in an applicable master's degree program.

Domestic applicants: USD 13,327 a year (2026-27). Arizona resident tuition. Tuition only for the graduate on-campus (Tempe/Downtown Phoenix/Polytechnic/West Valley) rate band in the ASU tuition estimator, 2026-27: Fall 2026 plus Spring 2027 term tuition summed (full-time flat rate; part-time credit loads Source

International applicants: USD 24,780 a year (2026-27). International tuition. Tuition only for the graduate on-campus (Tempe/Downtown Phoenix/Polytechnic/West Valley) rate band in the ASU tuition estimator, 2026-27: Fall 2026 plus Spring 2027 term tuition summed (full-time flat rate; part-time credit loads ar Source

Entry terms: Fall, Spring and Summer; the terms available for each programme are listed in Degree Search.

Graduate application deadlines are set by each programme office and listed in Degree Search; the application opens almost a year before the start term. The nonrefundable application fee is $75 for U.S. citizens, permanent residents, DACA and international online students outside the U.S., and $120 for international students on a nonimmigrant visa studying in the U.S. Apply with ASU's graduate application (Law applicants use the law college's site); GRE/GMAT requirements vary by programme.

Apply through the official page: https://admission.asu.edu/apply/graduate/admission

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