Data Science - MS

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College of Science

Department of Computer Science

Mathematics and Statistics

Requirements for the Master of Science in Data Science are listed below. Students must complete 30 credit hours, 12 of which must be at the 6000 level or above (excluding CS 6900 , CS 6250 , CS 6970 , STAT 6250 and STAT 6970 ). All students will complete a Plan A MS which includes the completion of a thesis that will be reviewed by their committee. An overall GPA of 3.0 or higher and individual course grades of B- or better (in all degree-program coursework) are required.

Students with an overall GPA below 3.0 at the completion of fall and spring semesters will be issued an academic warning. If a student’s institution or Program of Study GPA remains below 3.0 for two consecutive semesters, they will be dismissed from the program.

A bachelor’s degree from an accredited U.S. college or university or a similarly recognized international university is required for admission to and completion of a graduate program.

A master’s degree must be completed within six years of entering the degree program. Additional time may be requested by the student’s committee and submitted to the Vice Provost of Graduate Studies for review. Coursework over eight years old at the time of degree completion may not be used for a graduate degree unless it is revalidated.

At least 24-semester credits for a master’s degree must be from a committee-approved and a School of Graduate Studies-approved Program of Study from Utah State University.

Students must submit and obtain approval of academic forms required for master’s students by the School of Graduate Studies .

Required Courses

Students are required to complete the 1-credit seminar course CS 6900 and 6 credits of either CS 6970 or STAT 6970 . Furthermore, students must take 9 credits from the following list of courses:

CS 5080 - Data Mining 3 credit(s)
CS 5665 - Machine Learning for Data Science 3 credit(s)
CS 5800 - Introduction to Database Systems 3 credit(s)
CS 6830 - Data Science in Practice 3 credit(s)
CS 6850 - Introduction to Data Analysis 3 credit(s)
CS 6675 - Advanced Data Mining 3 credit(s)
CS 6640 - Artificial Neural Networks 3 credit(s) or
STAT 6685 - Deep Learning Theory and Applications 3 credit(s)
MATH 5710 - Introduction to Probability 3 credit(s)
STAT 5050 - Introduction to R 1 credit(s)
STAT 5100 - Modern Regression Methods (CI/QI) 3 credit(s)
STAT 5650 - Statistical Learning and Data Mining I 2 credit(s)

Elective Courses

Students may complete the course credits for the degree by choosing courses from the required or elective list of courses. Only 3 credits of CS 6950 are allowed. CS 6890 and STAT 6810 are allowed only if the topic is in the field of Data Science. Only 3 credits of CS 6250 or STAT 6250 are allowed.

CS 6250 - Cooperative Work Experience, Graduate 1-9 credit(s) or
STAT 6250 - Graduate Internship/Co-op 1-8 credit(s)
CS 6040 - Scientific Visualization for Data Science 3 credit(s)
CS 6600 - Intelligent Systems 4 credit(s)
CS 6680 - Computer Vision: Foundations and Applications 3 credit(s)
CS 6800 - Advanced Database 3 credit(s)
CS 6820 - Interactive Information Visualization for Data Science 3 credit(s)
CS 6840 - Graph Mining 3 credit(s)
CS 6890 - Topics in Computer Science (Topic) 1-4 credit(s)
CS 6950 - Directed Readings in Computer Science 3 credit(s)
MATH 5720 - Introduction to Mathematical Statistics 3 credit(s)
STAT 5550 - Statistical Visualization I 2 credit(s)
STAT 6080 - Data Technologies 2 credit(s)
STAT 6100 - Advanced Regression Analysis 2 credit(s)
STAT 6410 - Applied Spatial Statistics 2 credit(s)
STAT 6550 - Statistical Computing 3 credit(s)
STAT 6710 - Mathematical Statistics I 3 credit(s)
STAT 6810 - Topics in Statistics (Topic) 1-3 credit(s)

Research Requirements

Form a supervisory committee.
Complete a graduate thesis proposal.
Prepare a graduate thesis based on original work.
Successfully defend and submit the thesis to the library.