The STEM-designated Bachelor of Science in Economics and Data Science, housed in the School of Economics at the LeBow College of Business and offered jointly with the Nick Howley College of Engineering and Computing, combines economic theory with data science techniques to prepare students for analytical reasoning in a data-intensive world.
You’ll gain insight into market mechanisms, incentive structures and causal inference while developing skills in predictive analytics, data analysis and computational problem-solving. Through this interdisciplinary program, you’ll learn to use data to evaluate complex economic, business and policy challenges.
The BS in Economics and Data Science combines rigorous training in economics with modern data science techniques to prepare you for a world where data informs nearly every business, policy and organizational decision. You’ll learn how to analyze markets, interpret complex data and build evidence-based solutions to real-world challenges.
Drexel LeBow’s BS in Economics and Data Science:
U.S. Bureau of Labor Statistics
School of Economics' Class of 2024
The BS in Economics and Data Science degree 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.
This course shows how “big data” can be used to address pressing social and economic problems, including: economic and racial inequality; education and upward mobility; health insurance and health outcomes; climate change; criminal justice; automation and globalization. Students gain an overview of “what economists do” plus hands-on introduction to data science and econometrics—skills useful for other classes and valued in the job market. Topics are presented intuitively without requiring prior coursework in data science or econometrics, making the course ideal for students exploring economics and data science for the first time, as well as advanced students who want to see apply concepts learned in other classes. As an elective, it provides additional depth for interested students.
Provides a practical introduction to applied machine learning (ML) with data and examples from real-world applications. Covers the full ML solutions lifecycle, including data exploration, feature engineering, model selection, hyperparameter tuning, and result interpretation. Introduces supervised and unsupervised learning, while exploring practical topics such as bias-variance tradeoffs, data leakage, and evaluation pitfalls. With widely-used Python libraries, the course applies clustering, classification, regression, and dimensionality reduction, and other ML tools to a range of problems, preparing students to study applied deep learning. Provides experience with reproducible experiments, project-based analysis, and model selection through assignments and a comprehensive term project.
This elective course explores data analytic methods for collecting, analyzing, and visualizing social media content to understand emerging trends across social, organizational, and cultural contexts. Students will work with diverse data types, including text, interactions, and networks, and apply techniques such as natural language processing, machine learning, statistical modeling, and network analysis. The course emphasizes connecting online behavior to underlying social processes and addressing ethical, methodological, and platform-specific challenges in studying digital traces. Students will learn data harvesting, API use, study/research design, and responsible interpretation of social data.
Provides a rigorous introduction to the foundations and modern methods of natural language processing (NLP). The course integrates linguistic, statistical, and machine-learning perspectives, beginning with core topics such as text representations, n-gram models, and supervised learning for language tasks. Students then explore neural models and attention distributions produced by transformer-based large language models. Emphasis is placed on the theoretical principles that govern these models, alongside practical considerations in training, fine-tuning, evaluating, and deploying them in real-world applications. Throughout the course, students complete a series of programming assignments and support a team project that builds, analyzes, and responsibly applies NLP methods.
This course covers state-of-the-art statistical techniques for identifying causal effects including matching models, field experiments, regression discontinuity, instrumental variables, event studies, fixed effects, difference in differences, partial identification, and simulation. Students will study these techniques by applying them to data used in published papers from a variety of fields in economics and social science (e.g., health, labor, environment, business, taxation).
Through the Drexel Co-op program, students alternate classroom learning with full-time professional experience through University-approved employers. With plans of study that allow for up to three co-ops, you can gain as much as 18 months of practical experience while building professional networks, developing career clarity and applying classroom knowledge in real-world settings.
Graduate with as much as 18 months of experience in the workplace
LeBow Class of 2024
Our students have done their co-ops in a variety of organizations, both locally and around the world.
Newlane Finance, PRSCO, Silver Management Group, Wolf Financial, Vanguard, Victrex
Co-op is just one way to gain real-world experience at LeBow. Many students also expand their experience through consulting courses, case competitions, research projects, international residencies, industry events, alumni mentorship and other hands-on learning opportunities.
Many of these opportunities are made possible through the Dana and David Dornsife Office for Experiential Learning, where students work on real and simulated projects with industry partners. Partners have included organizations such as Unilever, the Philadelphia 76ers, Boeing, PwC, and nonprofit and governmental agencies.
One of the best parts of LeBow is getting to meet and work with so many people that are willing to help and who you can look up to. I never thought I could present our research at a global conference — it was an incredible experience.
Isabella Santosusso ’21
Read more about IsabellaThe BS in Economics and Data Science prepares 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:
The program also provides a strong foundation for further study in economics, data science, business, public policy or related analytical disciplines.
This program is a strong fit for students who enjoy both quantitative analysis and understanding how people, businesses and markets make decisions. If you’re interested in using data to solve real-world economic, business or public policy challenges, you’ll develop the analytical and computational skills to turn complex information into meaningful insights.
You’ll build a foundation in economic analysis while learning data science techniques used to solve complex problems. Throughout the curriculum, you’ll develop skills in predictive analytics, structured and unstructured data analysis, quantitative modeling, hypothesis development, data processing workflows and communicating analytical insights to support evidence-based decision-making.
Organizations use data to guide strategy, evaluate performance and solve complex challenges. By combining economics with data science, you’ll learn to analyze markets and organizations, interpret data, build analytical models and communicate findings that support informed decision-making. These transferable skills prepare you for analytical roles across business, technology, consulting, government and nonprofit organizations, as well as graduate study.
All undergraduate applications are processed through Drexel University’s central admissions portal. Have questions about applying to LeBow? Our undergraduate enrollment team is here to help.
Find the information you need to plan for your education through Drexel Central. Review tuition and fees, estimate the cost of attendance, and explore financial aid resources, including scholarships, grants, loans and payment options.
Our staff is ready to help you with any questions you may have about the application process, including which program is right for you. We look forward to hearing from you.
Caitlin Brady
Director, Undergraduate Programs and Recruitment
(215) 571-3570 cem398@drexel.eduGerri C. LeBow Hall 333
