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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 can complete a master's degree by pursuing any of one of the three options: coursework, project, or 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. Proposals for a master's project or 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 |
Options
Complete one of the following options:
Coursework Option
Course List | Code | Title | Hours |
| 16 |
Project Option
Course List | Code | Title | Hours |
| DAMG 7945 | Master's Project | 4 |
| 12 |
Thesis Option
Electives
Course List | Code | Title | Hours |
| |
| |
| |
| |
| |
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
