Degree requirements

30 credit hours and a thesis, or

30 credit hours including the required capstone course (FSE 570)

Required Core (9 credit hours)

STP 502 Theory of Statistics II: Inference (3), EEE 554 Probability and Random Processes (3) or DSE 501 Statistics for Data Analysts (3)

CSE 511 Data Processing at Scale (3), CSE 512 Distributed Database Systems (3) or IFT 530 Advanced Database Management Systems (3)

Choose one from the following:

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)

Concentration (12 credit hours)

Complete one course for each of the following areas:

Data Analysis (3)

Optimization (3)

Machine Learning (3)

Data Assurance and Security (3)

Electives (3 or 6 credit hours)

Culminating Experience (3 or 6 credit hours)

CSE, IEE or SER 599 Thesis (6)

FSE 570 Data Science Capstone (3)

Additional Curriculum Information

Students should consult the academic unit for a list of approved electives and concentration course requirements.

Courses selected for Required Core or Concentration may not be used as elective coursework for the same plan of study. Students should check with their academic advisor to ensure that the total number of credit hours of their plan of study is equal to 30.

Admission 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 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:

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

2 LORs

Computing and industrial 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.