Economics and Computer Science MSECCS
Major: Economics and Computer Science
Degree Awarded: Master of Science in Economics & Computer Science (MSECCS)
Calendar Type: Quarter
Minimum Required Credits: 45.0
Classification of Instructional Programs (CIP) code: 30.3901
Standard Occupational Classification (SOC) code: 19-3011; 15-2051
About the Program
Advanced computing is disrupting the economy. Markets are increasingly moving to online platforms and machine learning and algorithms are replacing people in the provision of goods and services. Businesses and governments need leaders who understand the computer science that powers these new systems and who can also use economic theory and intuition to help design them.
The STEM-designated Drexel LeBow MS in Economics & Computer Science degree serves this need by combining training in advanced computation, data analysis, and economics to prepare students for careers at the interconnection of these two fields.
Additional Information
For more information please contact our Graduate Student Services department at lebowgradenroll@drexel.edu.
Admission Requirements
- Bachelor's degree
- GRE or GMAT
- Two letters of recommendation
- Statement of purpose
Additional Information
For more information, please contact Lori Miceli or call 215-895-0975.
Degree Requirements
| Economics Requirements | ||
| ECON 540 | Intro to Econometrics and Data Analysis | 3.0 |
| or STAT 610 | Statistics for Business Analytics | |
| ECON 548 | Mathematical Economics | 3.0 |
| ECON 550 | Econometrics | 3.0 |
| ECON 560 | Time Series Econometrics | 3.0 |
| ECON 610 | Microeconomics | 3.0 |
| ECON 614 | Macroeconomics | 3.0 |
| ECON 700 | Economics Seminar | 3.0 |
| Computer Science Requirements | ||
| Select six of the following courses: * | 18.0 | |
| Fundamentals of Databases | ||
| Introduction to Programming | ||
| Data Structures and Algorithms | ||
| Systems Basics | ||
| Introduction to Software Design | ||
| Introduction to Artificial Intelligence | ||
| Data Structures and Algorithms I | ||
| Data Structures and Algorithms II | ||
| Theory of Computation | ||
| Privacy | ||
| Advanced Artificial Intelligence | ||
| Machine Learning | ||
| Applications of Machine Learning | ||
| Deep Learning | ||
| Algorithmic Game Theory | ||
| Data Analysis at Scale | ||
| Applied Artificial Intelligence | ||
| Software Design | ||
| Experiential Learning Requirement | 3.0 | |
| Please select one (1) of the following: | ||
| Graduate Internship | ||
| International Business Seminar and Residency | ||
| Leading for Innovation | ||
| Business Consulting | ||
| Graduate-level electives | 3.0 | |
| Total Credits | 45.0 | |
Sample Plan of Study
Full Time
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| CS 501 | 3.0 | CS 502 | 3.0 | ECON 560 | 3.0 | VACATION | |
| CS 503 | 3.0 | CS 504 | 3.0 | CS Required elective | 3.0 | ||
| ECON 540 | 3.0 | ECON 550 | 3.0 | Experiential Learning Course | 3.0 | ||
| 9 | 9 | 9 | 0 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | ||||
| ECON 548 | 3.0 | ECON 614 | 3.0 | ||||
| ECON 610 | 3.0 | ECON 700 | 3.0 | ||||
| CS Required elective | 3.0 | Elective | 3.0 | ||||
| 9 | 9 | ||||||
| Total Credits 45 | |||||||
Part Time
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| CS 501 | 3.0 | CS 502 | 3.0 | CS 503 | 3.0 | CS 504 | 3.0 |
| ECON 540 | 3.0 | ECON 550 | 3.0 | ECON 560 | 3.0 | Experiential Learning Course | 3.0 |
| 6 | 6 | 6 | 6 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| ECON 548 | 3.0 | ECON 614 | 3.0 | CS Required Electives | 6.0 | Elective* | 3.0 |
| ECON 610 | 3.0 | ECON 700 | 3.0 | ||||
| 6 | 6 | 6 | 3 | ||||
| Total Credits 45 | |||||||
- *
Note: Second Year Summer is less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students this term.
Note: Some terms are less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students these terms.
Program Level Outcomes
- Students will acquire expertise in managing and analyzing big data using advanced computational and econometric techniques
- Students will understand how to use research design and econometrics to make casual inferences.
- Students will be able to integrate economics and computer science in order to design marketplace platforms including pricing mechanisms.
- Students can communicate to a variety of stakeholders how economics and computer science are applied to create market platforms and facilitate cyberexchange.