Why Penn State
Big data is growing exponentially in today’s business world and keeping up requires adaptability. That’s why we designed our Master of Professional Studies in Data Analytics (MPS-DAAN) and Master of Science in Data Analytics (MS-DAAN) degrees to combine the best of in-person and online education. Flexible, seven-week courses let you balance your busy schedule while still reaping the benefits of once-per-week face-to-face classes, like networking and developing interpersonal skills.
Based in the tech-savvy greater Philadelphia region, our programs cultivate the skills to collect, classify, analyze, and model data. Both programs are STEM designated, allowing 36 months total of Optional Practical Training (OPT) compared to 12 months for non-STEM degrees.
Programs Differences
Master of Professional Studies |
Master of Science |
|---|---|
| Capstone project completed in one semester | Thesis with public defense completed over at least two semesters |
| Preparation for data-driven careers | Preparation for research and Ph.D. programs |
| Full-time completion in 1.5 years | Full-time completion in 2 years |
| Part-time completion in 3 to 4 years | Part-time completion in 3 to 4 years |
| Students typically already have work experience | Students typically are fresh out of undergraduate studies |
| Applicants can request a GRE waiver | GRE score required for admission |
Each 30-credit program offers opportunities to collaborate with the campus’ Big Data Lab or industry partners while drawing on statistics, computer science, machine learning, and software engineering. Depending on your career or professional goals, you may choose to focus on practical application (MPS-DAAN) or research (MS-DAAN).
Master of Professional Studies in Data Analytics
Intended for professionals looking to acquire analytical skills relevant to the workplace, the curriculum consists of six required courses and three electives. The program culminates in a capstone course that builds upon the theories, technology, and skills learned in previous coursework. Projects encompass a multitude of areas, including creating an analytical pipeline to predict Alzheimer's Disease, detecting mask compliance in real-time, analyzing character networks and relationships in literature, predicting high-risk borrowers, identifying and deleting duplicate questions on websites with user-generated content, and more.
Possible careers include data scientists, modelers, analysts, or architects.
Master of Science in Data Analytics
Through its emphasis on research, this program is designed to prepare you for entry into doctoral programs in data analytics. The curriculum has five required courses and three electives. You’ll take a research methods class and must pass SARI requirements prior to graduation. Rather than a capstone course, the program requires a thesis paper, which is six credits taken over two terms, and the completion of CITI training. You’ll work with an assigned faculty adviser for the course of your research. To graduate, the thesis must be accepted by a committee, the head of the graduate program, and the chancellor. Recent thesis papers have focused on information extraction and simplification from complicated medical notes, predicting Autism Spectrum Disorder based on genetic profiles, economic loss caused by disruption in infrastructure networks, and more.
