Data Science, Analytics and Engineering (Bayesian Machine Learning), MS
Turn complex data into powerful predictions as you learn the probabilistic frameworks that are shaping the rapidly changing artificial intelligence landscape. In a world saturated with information and uncertainty, you'll use Bayesian methods to extract meaningful insights where traditional approaches fail. Develop the sought-after expertise that organizations value.
Quick facts
Locations
Credits
Additional facts
STEM-OPT extension eligible: Yes
International students (F1) can extend U.S. work experience by 24 months.
Accreditation
Accredited by the Higher Learning Commission.
Degree description
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. With its programs in statistics, applied mathematics and theoretical mathematics, the school is distinctly positioned to enable you to understand the statistical, probability and mathematical bases for the technical tools and emerging concepts in statistical and probabilistic machine learning and data science. The school also supports your ability to collect, maintain, analyze, model and decide based on heterogeneous, time-dependent, noisy, biased, hierarchical and potentially large data sets.
Bayesian thinking is particularly suited to addressing the difficult issues associated with these high-dimension complex modeling challenges. Bayesian learning, decision-making and computation have made a significant impact on many areas of data science. Hierarchical modeling, time series analysis, ensemble modeling, spatial modeling and causal modeling are among the various areas of expertise covered by this program. You can apply these tools in a variety of domains, including engineering, physics, biology, social sciences, economics and finance.
You perform Bayesian data analysis, modeling, remodeling, and decision-making in data-enriched environments. The curriculum includes the exploratory analysis of massive and complex data streams, Bayesian modeling and computing, data management, causal modeling, and inference and decision under uncertainty using Bayesian trees, neural networks and text modeling popular in industry and academia.
Curriculum
This graduate degree includes courses focused on a specialized topic, so you can advance your skills for further professional opportunities. Explore all courses below.
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 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 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)
STP 502 Theory of Statistics II: Inference (3)
STP 505 Bayesian Statistics (3)
STP 540 Computational Statistics (3)
STP 551 Time Series Analysis (3)
FSE 570 Data Science Capstone (3)
STP 599 Thesis (6)
For concentration coursework, students select three courses from the list. Students should consult the academic unit for a list of approved electives.
Admission requirements
Here are the admission and application details
All students are required to meet general university admission requirements.
U.S. applicants (opens in new window) | International applicants (opens in new window) | English proficiency (opens in new window)
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. Applicants are required to submit: Additional Application Information All applicants must demonstrate relevant coursework or experience in the following three areas: Completion of an undergraduate linear algebra course (e.g. MAT 343 Applied Linear Algebra) before applying is a firm requirement for this concentration. In addition, applicants who do not have 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:
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.
Application deadlines
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 12/31 | Priority* |
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 07/31 | Priority* |
*A priority deadline means that applications submitted and completed before the priority deadline will receive priority consideration. Applications submitted after the priority deadlines will be reviewed in the order in which they were completed and on a space-available basis. An application is complete after all materials are received by Admission Services.
Explore expanded study opportunities
This program allows you to obtain both a bachelor's and a master's degree in as little as five years. As a high-achieving student, accelerated bachelor's plus master's degree programs are designed for you to share undergraduate coursework with graduate coursework to accelerate completion of your master's degree. These programs feature the same high-quality curriculum taught by ASU's world-renowned faculty.
This program is offered as an accelerated bachelor's plus master's degree with:
Approval to pursue the accelerated master's is typically granted during the junior year of your bachelor's degree program. If you are interested, you can learn more about eligibility requirements and how to apply.
Paying for college
Use our calculator to estimate your full-time or part-time tuition fees for this program prior to any financial aid. Keep in mind that most of our students receive financial aid, which can reduce out-of-pocket costs.
Get an estimateCareer opportunities
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. Professionals with data science skills are needed in financial markets and at central banks; in the pharmaceutical, semiconductor, communications, energy, and power systems sectors; and at institutions such as the National Institutes of Health, the Centers for Disease Control and Prevention, and the National Oceanic and Atmospheric Administration.
Contact information
Use the contact information below to get in touch if you're a current student or have general questions.
Prospective students should fill out the Request for Information form.
Electrical Engineering Program
|
WXLR A213
grad.math@asu.edu
480-965-3951
Admission deadlines
Frequently asked questions about degree programs
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.
Joint programs, or jointly conferred degrees, are offered by more than one college and provide opportunities for students to take advantage of the academic strengths of two academic units. Upon graduation, students are awarded one degree and one diploma conferred by two colleges.
ASU adds new programs to Degree Search frequently. Come back often and look for the "New Programs" option.
ASU Online offers programs in an entirely online format with multiple enrollment sessions throughout the year. See https://asuonline.asu.edu/ for more information.
The Western Regional Graduate Program (WRGP) provides a reduced tuition rate to non-resident graduate students who qualify. Visit the WRGP/WICHE webpage for more information: https://graduate.asu.edu/wiche.
