Statistics BS
Degree Awarded: Bachelor of Science (BS)
Minimum Required Credits: 120.0
Co-op Option(s): Two Co-op, One Co-op, No Co-op
Classification of Instructional Programs (CIP) code: 27.0502
Standard Occupational Classification (SOC) code: 15-2041
About the Program
Statistics is a rapidly expanding discipline at the center of today’s data-driven world. As industries and research fields increasingly rely on data to guide decisions, the ability to collect, analyze, and interpret information has become essential. From agriculture and medicine to technology, public health, engineering, media, and finance, statisticians play a critical role in transforming raw data into meaningful insights. Whether a streaming platform determines which shows to greenlight based on viewer patterns or a school district evaluates the impact of a new curriculum, these decisions depend on statistical thinking and evidence based problem solving.
The Bachelor of Science in Statistics at Drexel equips students with a strong foundation of statistical theory, computational skills, and applied data analysis. Students learn the mathematical principles underlying classical statistical methods, such as regression analysis and principal component analysis, as well as machine learning algorithms and modern data science techniques. This combination of theory and computation enables students not only to understand statistical tools but also to implement them effectively using industry standard software and programming languages.
A defining feature of the major is its emphasis on applying statistical methods to real world problems. Students gain hands on experience working with authentic data sets and selecting elective courses from the natural sciences, social sciences, and applied fields. These electives provide opportunities to explore specialized applications such as public health analytics, environmental modeling, economic data analysis, or scientific experimentation. Additional upper level options, including Monte Carlo methods, stochastic processes, and machine learning, allow students to deepen their expertise according to their interests.
Graduates of the program are well prepared for a broad range of career paths. Statisticians are in demand across sectors that depend on rigorous data analysis and predictive modeling. The major also provides a strong analytical foundation for students who wish to pursue advanced study in statistics, biostatistics, data science, economics, finance, or other quantitative disciplines.
Overall, the undergraduate statistics major offers students the tools, perspective, and versatility to harness the power of data and contribute meaningfully to a world increasingly shaped by statistical reasoning and informed decision making.
Additional Information
For more information about this program, please contact the College of Arts and Sciences
Degree Requirements
| University Level Degree 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: | 6.0 | |
Introductory: Inquire and Analyze | ||
Introductory: Collaborate and Integrate | ||
Introductory: Apply and Engage -satisfied by AS-I 1007 in the College Requirements below. | ||
| Free Electives | 15.0 | |
| College Level Degree Requirements | ||
| AS-I 1007 | Journeys in Arts and Sciences | 3.0 |
| AS-I 1008 | Data Fluency | 3.0 |
| AS-I 1009 | Interdisciplinary Ethics | 3.0 |
| Elementary Statistics Requirement | ||
| MATH 1801 | Scientific Data Analysis | 3.0 |
| or STAT 2201 | Business Statistics I | |
| Mathematics Requirements | ||
| MATH 1201 | Calculus I | 4.0 |
| MATH 1202 | Calculus II | 4.0 |
| MATH 2101 | Introduction to Mathematical Reasoning | 3.0 |
| MATH 2201 | Multivariable Calculus | 4.0 |
| MATH 2401 | Linear Algebra I | 3.0-4.0 |
| or MATH 2901 | Matrix and Differential Systems I | |
| MATH 2801 | Probability and Statistics I | 3.0 |
| MATH 2802 | Probability and Statistics II | 3.0 |
| MATH 3211 | Real Analysis I | 3.0 |
| MATH 3801 | Statistical Theory I | 3.0 |
| MATH 3831 | Statistical Computing | 3.0 |
| External Requirements | ||
| CS 1020 | Introduction to Computer Programming | 3.0 |
| or CS 1030 | Computer Science I | |
| STAT 3335 | Introduction to Experimental Design | 3.0 |
| Mathematics Electives | ||
| Select three (3) of the following courses: | 9.0 | |
| Actuarial Science: Financial Mathematics | ||
| Actuarial Science: Probability | ||
| Numerical Linear Algebra | ||
| Optimization | ||
| Mathematical Finance | ||
| Stochastic Processes I | ||
| Monte Carlo Methods | ||
| Topics in Statistics | ||
| Real Analysis II | ||
| Statistical Theory II | ||
| Stochastic Processes II | ||
| Independent Study in Mathematics | ||
| Applied Electives | ||
| Select two (2) of the following courses: | 6.0 | |
| Bioinformatics | ||
| Genomics | ||
| Data-Driven Decision Models | ||
| Machine Learning for Business | ||
| Using Big Data to Solve Economic and Social Problems | ||
| Applied Econometrics | ||
| Time Series Econometrics | ||
| Experiments and Causality in Economics | ||
| Applied Machine Learning | ||
| Principles of Optimization and Inference | ||
| Applied Deep Learning | ||
| Big Data Physics | ||
| Quantitative Research Methods in Politics | ||
| Research Design - Quantitative Methods | ||
| Free Electives | 24.0 | |
| 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 | 120.0-121.0 | |
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A complete list of eligible Introductory Core Competency courses can be found here.
Program Level Outcomes
Upon completion, students will be able to:
- Understand the mathematical foundations of statistical theory and produce valid statistical arguments.
- Apply statistical techniques to collect and analyze real-world data from a variety of disciplines and draw context-appropriate conclusions.
- Identify common statistical pitfalls and assess the validity of statistical arguments.
- Communicate statistical analysis clearly, both orally and in writing, at levels appropriate for experts and general audiences.
- Interact effectively with collaborators in other disciplines.
- Demonstrate substantial computer programming skills and proficiency in the use of statistical software to perform data analysis.