Systems Science and Engineering, BSE
Systems Engineers provide technical management for societal-scale problems that often encompass the connections between the physical and the information world. Examples of the many cutting-edge applications include autonomous robotics, smart buildings, national power grid management, global networks, service optimization, and biological systems. Systems engineering is the set of reusable mathematics, intellectual tools, and methodologies for attacking large-scale engineering problems. These common tools are adaptable for problems in different engineering domains (e.g., electrical, mechanical, biological, chemical, and computing) and help us understand, design, and manage systems that contain elements from multiple domains. Systems engineering deals with how we extract useful, abstract models from lower level systems, use these models to analyze and predict behavior, and use the analysis to control behavior and optimize/synthesize solutions. System engineering helps us understand what happens when we compose many elements, each with their own behavior, and how to design and constrain the individual elements to engineer desired behavior for the composed system.
For more information: https://www.seas.upenn.edu/prospective-students/undergrad/majors/systems-science-and-engineering/
Systems Science and Engineering (SSE) Major Requirements
37 course units are required.
| Code | Title | Course Units |
|---|---|---|
| Engineering | ||
| Systems Foundations | ||
| CIS 1100 | Introduction to Computer Programming (or equivalent) | 1 |
| or ENGR 1050 | Introduction to Scientific Computing | |
| ESE 1110 | Atoms, Bits, Circuits and Systems 1 | 1 |
| CIS 1200 | Programming Languages and Techniques I | 1 |
| ESE 2100 | Introduction to Dynamic Systems | 1 |
| ESE 2240 | Signal and Information Processing | 1.5 |
| ESE 3030 | Stochastic Systems Analysis and Simulation | 1 |
| ESE 3040 | Introduction to Optimization | 1 |
| Information Systems Electives | ||
| Select 3 from the following: | 3 | |
| Introduction to Computer Systems | ||
| Database and Information Systems | ||
| Artificial Intelligence Lab: Data, Systems, and Decisions | ||
| Foundations of Data Science | ||
| Fourier Analysis and Applications in Engineering, Mathematics, and the Sciences | ||
| Introduction to Networks and Protocols | ||
| Linear Systems Theory | ||
| Feedback Control Design and Analysis | ||
| Introduction to Optimization Theory | ||
| Dynamical Systems for Engineering and Biological Applications | ||
| Graph Neural Networks | ||
| Estimation and Detection Theory | ||
| Digital Signal Processing | ||
| Data Mining: Learning from Massive Datasets | ||
| Principles of Deep Learning | ||
| Modern Convex Optimization | ||
| Combinatorial Optimization | ||
| F1/10 Autonomous Racing Cars | ||
| Learning for Dynamics and Control | ||
| Model Predictive Control | ||
| Learning in Robotics | ||
| Information Theory | ||
| Scalable and Cloud Computing | ||
| Theory of Networks | ||
| Algorithmic Game Theory | ||
| Systems Project | ||
| Select one of the following: | 1 | |
| Introduction to Electrical and Systems Engineering Research Methodology | ||
| Deep Learning: A Hands-on Introduction | ||
| Embedded Systems/Microcontroller Laboratory | ||
| TinyML: Tiny Machine Learning for Embedded Systems 2 | ||
| Control For Autonomous Robots | ||
| Feedback Control Design and Analysis 2 | ||
| Biomechatronics 2 | ||
| Medical Devices 2 | ||
| ESE 4500 | Senior Design Project I - EE and SSE | 1 |
| ESE 4510 | Senior Design Project II - EE and SSE | 1 |
| Engineering Elective | 1 | |
| Engineering Elective 200 Level or above | 1 | |
| Math and Natural Science | ||
| MATH 1400 | Calculus, Part I | 1 |
| MATH 1410 | Calculus, Part II | 1 |
| MATH 2400 | Calculus, Part III 3 | 1 |
| or ESE 2030 | Linear Algebra with Applications to Engineering and AI | |
| ESE 3010 | Engineering Probability | 1 |
| ESE 4020 | Statistics for Data Science | 1 |
| or ESE 5420 | Statistics for Data Science | |
| PHYS 0140 | Principles of Physics I (without laboratory) | 1-1.5 |
| or PHYS 0150 | Principles of Physics I: Mechanics and Wave Motion | |
| or PHYS 0170 | Honors Physics I: Mechanics and Wave Motion | |
| or MEAM 1100 | Introduction to Mechanics | |
| ESE 1120 | Engineering Electromagnetics | 1.5 |
| or PHYS 0141 | Principles of Physics II (without laboratory) | |
| or PHYS 0151 | Principles of Physics II: Electromagnetism and Radiation | |
| or PHYS 0171 | Honors Physics II: Electromagnetism and Radiation | |
| CHEM 1012 | General Chemistry I | 1 |
| or EAS 0091 | Chemistry Advanced Placement/International Baccalaureate Credit (Engineering Students Only) | |
| or BIOL 1101 | Introduction to Biology A | |
| or BIOL 1121 | Introduction to Biology - The Molecular Biology of Life | |
| MATH 3140 | Advanced Linear Algebra | 1 |
| or MATH 3700 | Algebra | |
| Natural Science Lab (if applicable) 4 | .5 | |
| Professional Electives | ||
| Technology Management Electives | ||
| ESE 4000 | Engineering Economics | 1 |
| or EAS 5450 | Engineering Entrepreneurship I | |
| or EAS 5950 | Foundations of Leadership | |
| or MGMT 2370 | Management of Technology | |
| or OIDD 2360 | Scaling Operations in Technology Ventures: Linking Strategy and Execution | |
| Societal Problem Application | ||
| Select 3 Societal Problem Electives 5 | 3 | |
Biological Systems | ||
| Physical Models of Biological Systems | ||
| Principles of Molecular and Cellular Bioengineering | ||
| Nanoscale Systems Biology | ||
| Networked Neuroscience | ||
| The Mathematics of Medical Imaging and Measurement | ||
| Introduction to Computational Biology & Biological Modeling | ||
Human Factors | ||
| Human Systems Engineering | ||
Climate | ||
| Climate Policy and Technology | ||
| Engineering and the Environment | ||
| Risk Analysis and Environmental Management | ||
| Risk Management | ||
| Sustainable Goods | ||
Energy | ||
| Energy and Its Impacts: Technology, Environment, Economics, Sustainability | ||
| Renewable Energy and Its Impacts: Technology, Environment, Economics, Sustainability. | ||
| Energy Systems and Policy | ||
| Introduction to Energy Policy | ||
| Direct Energy Conversion: from Macro to Nano | ||
Quantitative Modeling | ||
| Operations Management Analytics | ||
| Advanced Decision Systems: Evolutionary Computation | ||
| Mathematical Modeling and its Application in Finance | ||
| Data Science for Finance | ||
| Market Design | ||
| Applied Econometrics I | ||
City Planning | ||
| Quantitative Planning Analysis Methods | ||
| Planning by Numbers | ||
| Introduction to Housing, Community and Economic Development | ||
| Introduction to Transportation Planning | ||
| Metropolitan Food System | ||
Transportation | ||
| Introduction to Transportation Planning | ||
| Transportation Planning Methods | ||
| The Practice of Trans.Plng:Crafting Policies & Bldg. Infrastructure | ||
| Advanced Transportation Seminar | ||
Chemical Processing | ||
| Molecular Modeling and Simulations | ||
Communications | ||
| Introduction to Networks and Protocols | ||
Robotics | ||
| Design of Mechatronic Systems | ||
| Introduction to Robotics | ||
| Advanced Robotics | ||
Machine Intelligence | ||
| Applied Machine Learning | ||
| Machine Learning | ||
| Artificial Intelligence | ||
| Computer Vision & Computational Photography | ||
| Principles of Deep Learning | ||
| Learning in Robotics | ||
| Applied Probability Models in Marketing | ||
| General Electives 6 | ||
| EAS 2030 | Engineering Ethics (or equivalent) | 1 |
| or LAWM 5060 | ML: Technology Law | |
| or CIS 4230 | Ethical Algorithm Design | |
| Select 4 Social Science or Humanities courses | 4 | |
| Select 2 Social Science or Humanities or Technology in Business & Society courses | 2 | |
| Total Course Units | 37 | |
- 1
If not taken by the end of freshman year, must be replaced by another department approved Engineering course.
- 2
If ESE 5050 or BE 4700 or BE 5700 or ESE 3600 is taken, an additional .5 CU engineering credit is required.
- 3
If MATH 2400 is taken, ESE 2030 will not count. If ESE 2030 is taken, MATH 2400 will not count.
- 4
This category requires 10 CU, including two .5 CU Natural Science Labs. Several of the courses above are 1.5 CU and already include .5 CU Natural Science Lab. If the courses selected do not total 10 CUs, you will be required to complete the additional CUs required with up to two .5 CU Natural Science Labs from the following list: BIOL 1124 Introductory Organismal Biology Lab, CHEM 1101 General Chemistry Laboratory I, MEAM 1470 Introduction to Mechanics Lab, PHYS 0050 Physics Laboratory I, PHYS 0051 Physics Laboratory II, or another department approved Natural Science Lab.
- 5
A complete list of approved SPA electives can be found on the ESE undergraduate programs webpage
- 6
Must include a Writing Seminar (a list of approved Writing Seminars can be found in the SEAS Undergraduate Handbook)
Concentrations
Students may select one of three concentrations:
- Data Science and Artificial Intelligence
- Decision Science
- Robotics
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.