Admissions to this program will open for the 2026-2027 academic year.
For program contact information, please visit this website.
Many MS programs in the data area deal with data collection and analysis but do not, however, address a crucial activity that data scientists, data analysts, business analysts, and many software engineers need to perform to make that data valuable—data integration. That activity may also be referred to as data preparation, data curation, application integration, and data engineering based on the integration of use cases and integration persona. The Master of Science in Data Architecture and Management focuses on these activities.
Data systems engineering occurs because data is fragmented and usually scattered across many data sources. However, even if all the data one needed were in one place, there is still an intensive need for integration. Information is data in context and the context of data as collected is different than the many ways it needs to be transformed so as to generate useful information.
The data engineering field could be thought of as a superset of business intelligence and data warehousing that brings in more elements from software engineering. This discipline also integrates specialization around the operation of so-called Big Data distributed systems, along with concepts around the extended Hadoop ecosystem, stream processing, and in computation at scale.
The Master of Science in Data Architecture and Management offers a multitude of courses in data engineering in addition to supplementary courses that are required to deliver the data results in a meaningful way to management. We plan to cover data management, advanced data management, data warehousing and business intelligence, column databases, data science engineering, and Big Data engineering. On the software engineering side, we offer advanced Big Data 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 hundredfold increases in speed. Due to the high-level mathematical operations required to make these programs run, only software engineers can make the necessary mathematical algorithms execute quickly enough to work in these complicated areas and get the finest results.
Degree Requirements
Students in the program must complete 32 semester hours of approved coursework with a minimum grade-point average of 3.000. Students complete a master's degree by pursuing a thesis.
The thesis must be carried out under the supervision of a professor and must have prior approval of the program director. Proposals for a thesis need to be submitted at least one month before the start of the semester.
Complete all courses and requirements listed below unless otherwise indicated.
Core Requirements
Course List | Code | Title | Hours |
| DAMG 6105 | Data Science Engineering with Python | 4 |
| DAMG 6210 | Data Management and Database Design | 4 |
| DAMG 7250 | Big Data Architecture and Governance | 4 |
| DAMG 7370 | Designing Advanced Data Architectures for Business Intelligence | 4 |
Thesis
Electives
Course List | Code | Title | Hours |
| 8 |
| Big-Data Systems and Intelligence Analytics | |
| Advanced Database Management Systems | |
| Systems and Cybersecurity Fundamentals | |
| Designing Advanced Data Architectures for Business Intelligence | |
| Special Topics in Data Architecture and Management | |
| Concepts of Object-Oriented Design | |
| Concepts of Object-Oriented Design with C++ | |
| Enterprise Software Design | |
| Network Structures and Cloud Computing | |
| Operating Systems | |
| 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 | |
| Software Engineering | |
| Building Virtual Environments | |
| User Experience Design and Testing | |
| Deep Learning and Reinforcement Learning in Game Engineering | |
| Special Topics in Computer Systems Engineering | |
| Advanced Game Analytics | |
| Distributed Intelligent Agents in the Metaverse | |
| Application Engineering and Development | |
| Lab for INFO 5100 | |
| Data Science Engineering Methods and Tools | |
| Web Design and User Experience Engineering | |
| Program Structure and Algorithms | |
| Web Development Tools and Methods | |
| Accounting and Budgetary Systems for Engineers | |
| Agile Software Development | |
| Advanced Big-Data Applications and Indexing Techniques | |
| Business Process Engineering | |
| 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
