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The software engineering systems program takes a sociotechnical, engineering approach to software. This engineering foundation is designed to enable students to embrace real-world complexity as a golden opportunity, especially for the more technically advanced student. We are committed to shaping our students to be intuitive problem solvers, experienced engineering architects, and result leaders who will have a great impact at the exciting three-way intersection of computer science, engineering, and ethics.
Our program offers a multitude of courses in Big Data engineering and analytics in addition to supplementary courses that are required to deliver the data analytics results in a meaningful way to management. We cover data management, advanced data management, business intelligence, column databases, data science, and Big Data engineering. We offer advanced functional programming using the powerful Scala language and a course on advanced data science as well as cloud computing. Multithread concurrent computing is also offered as it is important for synchronizing a huge set of servers working in parallel to do large-scale analytics to make things run faster by a hundredfold increase in speed. Due to the high-level mathematical operations required to run these programs, only software engineers have the capacity to work in such complicated areas. Only they can make the necessary mathematical algorithms execute quickly enough to get the finest results.
Our engineers become fluent in data science for the sake of building the actual system. They study how to write machine-learning algorithms on top of statistical packages.
- Students study the fundamentals of logical computing formulation and program construction as well as the mathematical modeling and analysis of algorithms—an essential aspect of data science analytics.
- Students study clustering techniques, along with topic modeling and classification and logical regression techniques, as well as Bayesian statistics.
- Students study how to configure and operate a Hadoop environment (large clusters of commodity hardware) and in the process how to integrate data from diverse sources to move and manage data through Big Data platforms (in-house or in the cloud). Data ingestion, the filtering and firing of millions of operations to run over large clusters of commodity hardware, is a software engineering technique that we teach our students how to perform through Scala, multithreading, Spark programming, and “map-reduce” techniques.
- We show students how to make the business case for analytics projects and how to follow an execution road map that involves understanding the architectures underpinning such gigantic platforms as well as the resourcing and cost issues.
Degree Requirements
Students in the program must complete 32 semester hours of approved coursework with a minimum grade-point average of 3.000. Students must complete the master's degree by pursuing a master's project or a thesis.
Master's project and thesis options must be carried out under the supervision of a professor and must have prior approval of the program director.
Beginning with students admitted in the fall of 2026, all students pursuing on-ground master's degree programs must complete a university-level experiential learning requirement. This requirement may be fulfilled through the curriculum, co-op, or other work- or industry-integrated learning experiences.
Complete all courses and requirements listed below unless otherwise indicated.
Core Requirements
Course List | Code | Title | Hours |
| |
| CSYE 6200 | Concepts of Object-Oriented Design | 4 |
| CSYE 7230 | Software Engineering | 4 |
| INFO 6205 | Program Structure and Algorithms | 4 |
Options
Complete one of the following options:
Project Option
Course List | Code | Title | Hours |
| CSYE 7945 | Master's Project | 4 |
| 4 |
| 12 |
Thesis Option
Restricted Electives
Course List | Code | Title | Hours |
| |
| Concepts of Object-Oriented Design with C++ | |
| Enterprise Software Design | |
| Network Structures and Cloud Computing | |
| Operating Systems | |
| Software Testing for Engineers | |
| Software Design Patterns and Engineering | |
| Introduction to Quantum Computing with Applications | |
| High-Performance Parallel Machine Learning and AI | |
| Advanced Cloud Computing | |
| Big-Data System Engineering Using Scala | |
| Foundations of Parallel, Concurrent, and Multithreaded Programming | |
| Deployment and Operation of Software Applications | |
| Building Virtual Environments | |
| User Experience Design and Testing | |
| Deep Learning and Reinforcement Learning in Game Engineering | |
| Theory and Practical Applications of AI Generative Modeling | |
| Advanced Game Analytics | |
| Directed Study | |
Other Electives
Course List | Code | Title | Hours |
| |
| Special Topics in Software Engineering Systems | |
| Concepts of Object-Oriented Design with C++ | |
| Enterprise Software Design | |
| Network Structures and Cloud Computing | |
| Operating Systems | |
| Software Testing for Engineers | |
| Software Design Patterns and Engineering | |
| Introduction to Quantum Computing with Applications | |
| High-Performance Parallel Machine Learning and AI | |
| Advanced Cloud Computing | |
| Deployment and Operation of Software Applications | |
| Building Virtual Environments | |
| User Experience Design and Testing | |
| Special Topics in Computer Systems Engineering | |
| Theory and Practical Applications of AI Generative Modeling | |
| Directed Study | |
| Data Science Engineering with Python | |
| Data Management and Database Design | |
| Big-Data Systems and Intelligence Analytics | |
| Big Data Architecture and Governance | |
| Advanced Database Management Systems | |
| Systems and Cybersecurity Fundamentals | |
| Designing Advanced Data Architectures for Business Intelligence | |
| Directed Study | |
| Application Engineering and Development
and Lab for INFO 5100 | |
| Data Science Engineering Methods and Tools | |
| Web Design and User Experience Engineering | |
| Business Analysis and Information Engineering | |
| Software Quality Control and Management | |
| Accounting and Budgetary Systems for Engineers | |
| Agile Software Development | |
| Engineering of Big-Data Systems | |
| Advanced Big-Data Applications and Indexing Techniques | |
| Business Process Engineering | |
| Special Topics in Information Systems | |
| Special Topics in Artificial Intelligence Engineering and Applications | |
| Managerial Communications for Engineers | |
| Advances in Data Sciences and Architecture | |
| Cryptocurrency and Smart Contract Engineering | |
Optional Co-op Experience
Course List | Code | Title | Hours |
| |
| ENCP 6000 | Career Management for Engineers | 1 |
| ENCP 6964 | Co-op Work Experience | 0 |
| or ENCP 6954 | Co-op Work Experience - Half-Time |
| or ENCP 6955 | Co-op Work Experience Abroad - Half-Time |
| or ENCP 6965 | Co-op Work Experience Abroad |
Program Credit/GPA Requirements
32 total semester hours required (33 with optional co-op)
Minimum 3.000 GPA required
