Economics and Data Science BSECDS
Degree Awarded: BS in Economics & Data Science (BSECDS)
Minimum Required Credits: 122.0
Co-op Option: Three Co-op, Two Co-op, One Co-op, No Co-op
Classification of Instructional Programs (CIP) code: 30.3901
Standard Occupational Classification (SOC) code: 15-2041
About the Program
The Bachelor of Science in Economics and Data Science is an interdisciplinary degree jointly administered by Drexel University’s LeBow College of Business and the College of Engineering and Computing. This STEM-designated program integrates economic theory with data science techniques to prepare students for analytical reasoning in a data-intensive world. Students explore how markets and institutions function and how data can be harnessed to understand behavior, evaluate decisions, and address complex societal and business challenges. Through this combination, graduates develop a capacity for evidence-based decision-making and quantitative insight into contemporary economic and organizational problems. The curriculum blends foundational study in microeconomics, macroeconomics, and economic statistics with core elements of data science, including problem definition, predictive analytics, and scalable data processing workflows.
Emphasis is placed on quantitative methods that support modeling, interpretation, and communication of economic and data-driven insights. Students cultivate skills in analyzing structured and unstructured data, applying predictive and causal frameworks, and using analytical tools to address real-world questions. Coursework advances competencies in hypothesis formulation, model building, and interpretation of results in contexts ranging from markets to public policy.
Drexel’s distinctive experiential education model offers students structured opportunities to integrate academic study with professional experience. Through the cooperative (co-op) education program, students can engage in full-time work experiences that align with their interests in economics and data science. Experiential learning opportunities such as co-op placements complement the degree by giving students opportunities to apply their analytical and computational skills to real-world organizational and policy challenges prior to graduation.
Graduates of the Economics and Data Science degree are prepared for a wide range of careers and graduate study pathways that rely on quantitative analysis, economic reasoning, and data-driven decision-making. Career directions may include roles involving economic analysis, data analytics, business or market research, policy evaluation, consulting, or analytical work in technology-focused organizations, as well as positions that support strategic planning or operational decision-making. The program also provides a strong foundation for further study in economics, data science, business, public policy, or related analytical disciplines, emphasizing transferable skills that support long-term adaptability across sectors.
Additional Information
For more information please contact the School of Economics
Degree Requirements
| University Requirements | ||
| EXP 1001 | Introduction to Experiential Learning | 3.0 |
| WRIT 1100 | Composition and Rhetoric I | 3.0 |
| or WRIT 1110 | English Composition I | |
| WRIT 1200 | Composition and Rhetoric II | 3.0 |
| or WRIT 1210 | English Composition II | |
| Introductory Core Competencies 1 | ||
| Select one course from each of the three (3) categories of Introductory Core Competency Courses: | 9.0 | |
Introductory: Inquire and Analyze | ||
Introductory: Collaborate and Integrate | ||
Introductory: Apply and Engage | ||
| Free Electives | 15.0 | |
| College Requirements | ||
| DLE 1101 | Dragon Awakening: Principles of Business | 3.0 |
| DLE 1201 | Dragon Development: Problem Solving in Practice | 3.0 |
| DLE 3301 | Dragon Transitions: Navigating Your Career Journey | 1.0 |
| ECON 1201 | Principles of Microeconomics | 3.0 |
| Program Requirements | ||
| CS 1020 | Introduction to Computer Programming | 3.0 |
| or CS 1030 | Computer Science I | |
| Select one of the following MATH sequences: | 7.0-8.0 | |
| Mathematical Tools and Techniques and Survey of Calculus | ||
| Precalculus and Calculus I | ||
| Calculus I and Calculus II | ||
| STAT 2201 | Business Statistics I | 3.0 |
| or MATH 2801 | Probability and Statistics I | |
| MATH 2401 | Linear Algebra I | 3.0 |
| or MATH 2901 | Matrix and Differential Systems I | |
| Economics Requirements | ||
| ECON 1202 | Principles of Macroeconomics | 3.0 |
| ECON 2250 | Game Theory and Applications | 3.0 |
| ECON 2301 | Microeconomics | 3.0 |
| ECON 2302 | Macroeconomics | 3.0 |
| ECON 2350 | Applied Econometrics | 3.0 |
| ECON 3360 | Time Series Econometrics | 3.0 |
| or ECON 3370 | Experiments and Causality in Economics | |
| ECON 4901 | Economics Seminar | 3.0 |
| Economic Free Electives | ||
| Select twelve (12) credits of ECON (Economics) 1000-4999 level courses | 12.0 | |
| Data Science Requirements | ||
| IS 1310 | Computing with Data | 3.0 |
| IS 2320 | Principles of Data Analysis | 3.0 |
| IS 2515 | Social and Ethical Aspects of Information | 3.0 |
| IS 3321 | Principles of Optimization and Inference | 3.0 |
| IS 3330 | Applied Machine Learning | 3.0 |
| IS 4311 | Data Workflow Automation and Pre-Processing | 3.0 |
| Select four (4) from the following: | 12.0 | |
| Computer Science II | ||
| Data Structures | ||
| Principles of Artificial Intelligence | ||
| Fundamental Machine Learning | ||
| Foundations of Deep Learning | ||
| Relational Database Management Systems | ||
| Cloud Computing and Scalable Processing | ||
| Data Visualization Principles and Practice | ||
| Applied Deep Learning | ||
| Human-Centered Design Process and Methods | ||
| Data Mining Applications | ||
| Deep Learning Architectures | ||
| Social Media Data Analysis | ||
| User Modeling and Recommender Systems | ||
| Modeling Natural Language | ||
| Network Data Analysis | ||
| Data Product Development | ||
| Intelligent Search and Language Models | ||
| Software Requirements and Modeling | ||
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. Co-op cycles may vary. Students choosing this option will be required to complete COOP 1001 as preparation for their co-op experience. COOP 1001 registration is determined by the co-op cycle assigned. | 0.0 | |
| Total Credits | 122.0-123.0 | |
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A complete list of eligible Introductory Core Competency courses can be found here.
