Admission

Graduate admission information

Concentration options

Applicants should apply to the program that best matches their undergraduate degree in order to ensure proper academic preparation for their program of study. Refer to the information below for the admission requirements.

Below are the concentration options and their foci.

• Bayesian machine learning: bayesian learning, decision-making, and computation.
• Computational mathematics and data: computational skills in optimization, machine learning, stochastic processes and dynamical systems.
• Computing and decision analytics: computing and industrial engineering.
• Electrical engineering: DSAE as it relates to electrical engineering.
• Human centered applications: human experience and needs with machine learning and AI applications.
• Materials science and engineering: DSAE as it relates to materials science and engineering.
• Mechanical and aerospace engineering: how data science interacts with concepts in mechanical and aerospace engineering, such as controls, energy systems, aeronautics, and mechanics.
• Sustainable engineering and built environment: DSAE as it relates to sustainable engineering and the built environment.

Application deadlines

Fall semester:

Preference is given to complete applications received by December 31. Admission results should be available by March 1. Applications received after this preferred deadline will be considered.

Spring semester:

Preference is given to complete applications received by July 31. Admission results should be available by October 1. Applications received after this preferred deadline will be considered.

Eligibility and GPA Requirements

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.

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.

Applicants are no longer required to submit Graduate Record Examinations, or GRE, scores as of fall 2023.

Application requirements

Applicants are required to submit:

1. graduate admission application and application fee
2. official transcripts
3. written statement
4. professional resume
5. proof of English proficiency

Recommended academic preparation

The Data Science, Analytics and Engineering program is a multidisciplinary program that requires students to take classes outside of their school. It is recommended that all applicants have the following background information prior to applying.

All applicants must demonstrate relevant coursework or experience in the following three areas:

1. 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)
2. Undergraduate linear algebra (e.g., MAT 242 Elementary Linear Algebra)
3. 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