SSE Degree Concentrations
Concentrations are focused areas of study that provide students with a critical level of expertise in a particular domain within SSE, preparing the student for employment or graduate school in that domain. To satisfy the requirements for a concentration, students must complete at least four courses from the selected concentration as detailed below.
*Students may only pursue one Concentration. Please note: Students who are submatriculating MUST meet all of the concentration requirements BEFORE obtaining their undergraduate degree.
Robotics
Must Complete the following course:
ESE 421 Control For Autonomous Robots
Select 3 from the following courses:
ESE 500 Linear Systems Theory
ESE 505 Feedback Control Design and Analysis
ESE 512 Dynamical Systems for Engineering and Biological Applications
ESE 619 Model Predictive Control
MEAM 520 Introduction to Robotics
MEAM 620 Advanced Robotics
Data Science and Artificial Intelligence
Must Complete the following course:
ESE 305 Foundations of Data Science
Select 3 from the following courses:
ESE 545 Data Mining: Learning from Massive Datasets
ESE 546 Priniples of Deep Learning
ESE 650 Learning in Robotics
CIS 520 Machine Learning
CIS 545 Big Data Analytics
NETS 312 Theory of Networks
ECON 262 Market Design
Decision Science
Decision Science (DS) is an interdisciplinary field that uses analytical methods to support better decisions. It employs techniques from probability, statistics and mathematical optimization. The concentration will equip students with the foundational knowledge and technical expertise need to deal with complex decision-making problems in organizations ranging from petrochemicals to airlines, finance, e-commerce, logistics, and government. The DS concentration will equip students with the foundational knowledge and technical expertise that enables data-driven insights to help organizations make better decisions.
Must Complete the following course:
ESE 504 Intro to Linear, Nonlinear and Integer Optimization
Select 3 from the following courses:
ESE 545 Data Mining: Learning from Massive Datasets
ESE 605 Modern Convex Optimization
NETS 312 Theory of Networks
OIDD 224 Analytics for Service Operations
OIDD 353 Mathematical Modeling and its Application in Finance
CIS 515 Fundamentals of Linear Algebra and Optimization
ESE 550 Advance Transportation Seminar, Air Transportation Planning
STAT 476 Applied Probability Models in Marketing
at most one from the following options:
- MATH 432 Game Theory
- NETS 412 Algorithmic Game Theory
- ECON 212 Game Theory
- ECON 682 Game Theory and its Applications