Data Science, Analytics and Engineering (Bayesian Machine Learning), MS
Big Data, Data Analysis, Data Science, Probability, approved for STEM-OPT extension, models, statistics
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.
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 students 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 students' 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. Students can apply these tools in a variety of domains, including engineering, physics, biology, social sciences, economics and finance.
Students 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.
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
This program allows students to obtain both a bachelor's and a master's degree in as little as five years. Accelerated bachelor's plus master's degree programs are designed for high-achieving students who want the opportunity to share undergraduate coursework with graduate coursework to accelerate completion of their 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:
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) 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.
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 a minimum cumulative GPA of 3.00 in an applicable master's degree program.
Applicants are required to submit:
- graduate admission application and application fee
- official transcripts
- written statement
- professional resume
- two letters of recommendation
- proof of English proficiency
Additional Application Information
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.
All applicants must demonstrate relevant coursework or experience in the following three areas:
- familiarity with MATLAB, Python, SQL, or R, or other relevant programming skills (in the professional resume)
- undergraduate linear algebra (e.g., MAT 343 Applied Linear Algebra)
- 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)
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:
- AWS-certified cloud practitioner
- Google data analytics certificate
- Google IT support certificate
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 12/31 | Priority |
| Session | Modality | Deadline | Type |
|---|---|---|---|
| Session A/C | In Person | 07/31 | Priority |
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.
Electrical Engineering Program
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WXLR A213
grad.math@asu.edu
480-965-3951
Admission deadlines
Program term definitions
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.
