|Person-in-Charge||Colin J. Neill|
The goal of this graduate certificate program is to prepare students to apply data analytics techniques to large data sets to support data-driven decisions across application domains.
Effective Semester: Spring 2020
Expiration Semester: Spring 2025
Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies. International applicants may be required to satisfy an English proficiency requirement; see GCAC-305 Admission Requirements for International Students for more information.
Applicants with undergraduate degrees in a quantitative discipline such as science, engineering, or business may apply. Students from other disciplines will be considered based on prior coursework. Applicants are generally expected to have a minimum combined junior/senior grade-point average of 3.0 (B) on a 4.0 scale.
Requirements listed here are in addition to requirements listed in Graduate Council policy GCAC-212 Postbaccalaureate Credit Certificate Programs.
To be awarded the Graduate Certificate in Data Analytics, students must successfully complete 15 credits of course work. All courses must be completed with a grade of C or better and a grade-point average of 3.0 to be awarded the certificate.
|STAT 500||Applied Statistics||3|
|SWENG 545||Data Mining||3|
|IE 575||Foundations of Predictive Analytics||3|
|DAAN 871||Data Visualization||3|
|Select one of the following:||3|
|Data-Driven Decision Making|
|Database Design Concepts|
|Network and Predictive Analytics for Socio-Technical Systems|
|Analytics Programming in Python|
Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.
Students will be able to:
- Effectively communicate technical knowledge, including ideas, data analysis, findings, or decision justification in written formats in a manner appropriate to the audience.
- Analyze large data sets to support data-driven decision making.
- Demonstrate understanding of machine learning and statistical analysis techniques.
|Graduate Program Head||Colin Neill|
|Director of Graduate Studies (DGS) or Professor-in-Charge (PIC)||Colin Neill|
Sharon V. Patterson