Data Analytics Graduate Credit Certificate Program

Admission Requirements

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.

Certificate Requirements

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.

Required Courses
STAT 500Applied Statistics3
SWENG 545Data Mining3
IE 575Foundations of Predictive Analytics3
DAAN 871Data Visualization3
Electives
Select one of the following:3
Data-Driven Decision Making
Database Design Concepts
Network and Predictive Analytics for Socio-Technical Systems
Deep Learning
Analytics Programming in Python
Total Credits15

Courses

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.

Learning Objectives

Students will be able to:

  1. Effectively communicate technical knowledge, including ideas, data analysis, findings, or decision justification in written formats in a manner appropriate to the audience.
  2. Analyze large data sets to support data-driven decision making.
  3. Demonstrate understanding of machine learning and statistical analysis techniques.

Contact

Campus Great Valley
Graduate Program Head Colin Neill
Director of Graduate Studies (DGS) or Professor-in-Charge (PIC) Colin Neill
Program Contact

Sharon Veronica Patterson
30 East Swedesford Road
Malvern PA 19355
svp40@psu.edu
(610) 648-3318

Program Website View