Data Science - Graduate Certificate

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College of Science

Department of Computer Science

Department of Mathematics and Statistics

Graduate Certificate in Data Science: Objectives and Audience

Data Science is an interdisciplinary field that includes the management, analysis, and visualization of data to make the best possible evidence-based decisions, and draws primarily from the fields of Statistics and Computer Science. Scientists and engineers in today’s workforce have vast amounts of data available to them, but too often they do not have sufficient Data Science expertise to make full use of the data. With so much of today’s STEM innovation being data-driven, expertise in collecting, analyzing, and operationalizing data is critical to industry success. The primary audience for this graduate certificate is graduate students or working professionals in STEM disciplines, who will enter the workforce with core science skills, and this Data Science graduate certificate will strengthen their ability to create Data Science tools and utilize them to add value in the workforce.

Admission

Students already pursuing a masters or doctoral degree at USU may apply for the Graduate Certificate in Data Science as a Secondary Program using the Concurrent Degree Form.

Students interested in pursuing the Graduate Certificate in Data Science as a standalone certificate (who are not already in a masters or doctoral degree program at USU) should apply through the USU School of Graduate Studies at https://gradschool.usu.edu/.

Because this certificate requires at least one 5000-level course in each of STAT and CS, students must have a sufficient background in probability and programming to be successful. Therefore, admission to this graduate certificate program requires an undergraduate or professional background that includes experience with statistics and computer programming.

Graduate Certificate Requirements

The Graduate Certificate in Data Science requires 12 total credits, including 3 credits from the Statistics Core, 3 credits from the Computer Science Core, 3 credits from the Implementation / Application core, and 3 elective credits from any of the three cores. For students simultaneously pursuing a masters or doctoral degree, at least 6 of the total credits for this graduate certificate must be outside the student’s home department, and (where approved by the student’s supervisory committee for their masters or doctoral degree) credits used towards this graduate certificate may also be used towards their masters or doctoral degree. All grades for courses applied toward the Data Science graduate certificate must be C or better.

Statistics

Students will take 3 credits from the following list:

STAT 5050 - Introduction to R 1 credit(s)
STAT 5080 - Data Technologies 2 credit(s)
STAT 5100 - Modern Regression Methods (CI/QI) 3 credit(s)
STAT 5200 - Analysis of Designed Experiments 3 credit(s)
STAT 5550 - Statistical Visualization I 2 credit(s)
STAT 5645 - Mathematical Methods for Data Science 3 credit(s)
STAT 5650 - Statistical Learning and Data Mining I 2 credit(s)
STAT 5685 - Deep Learning Theory and Applications 3 credit(s) or
STAT 6685 - Deep Learning Theory and Applications 3 credit(s)
STAT 6655 - Machine Learning 3 credit(s)
- A 5000-level or higher data science-based STAT class approved by the Data Science graduate certificate faculty advisory panel

Computer Science

Students will take 3 credits from the following list:

CS 5060 - Decision Making: Algorithms Under Uncertainty 3 credit(s)
CS 5080 - Data Mining 3 credit(s)
CS 5665 - Machine Learning for Data Science 3 credit(s)
CS 5820 - Interactive Information Visualization for Data Science 3 credit(s)
CS 6665 - Data Mining 3 credit(s)
CS 5830 - Data Science in Practice 3 credit(s) or
CS 6830 - Data Science in Practice 3 credit(s)
- A 5000-level or higher data science-based CS class approved by the Data Science graduate certificate faculty advisory panel

Implementation/Application

Students will take 3-4 credits from the following list:

CS 5510 - Robot Intelligence 4 credit(s)
CS 6675 - Advanced Data Mining 3 credit(s)
STAT 5555 - Advanced R Programming for Data Science 3 credit(s) or
STAT 6555 - Advanced R Programming for Data Science 3 credit(s)
- A 5000-level or higher data science-based application class in the student’s home department approved by the Data Science graduate certificate faculty advisory panel

Elective Credits

To reach the minimum 12 credits required for the Data Science graduate certificate, students will take 3 elective credits from any of the courses listed above in the Statistics, Computer Science, or Implementation/Application cores. For students simultaneously pursuing a masters or doctoral degree, at least 6 of the total credits for this graduate certificate must be outside the student’s home department.