Artis College of Science and Technology
- College of Business and Economics
- College of Education and Human Development
- College of Graduate Studies and Research
- Waldron College of Health and Human Services
- College of Humanities and Behavioral Sciences
- Artis College of Science and Technology
- College of Visual and Performing Arts
- Other Offices and Departments
- Biology Department
- Pre-Health Advisory Committee
- GIS Center
- Museum of the Earth Sciences
- Mathematics and Statistics
- Chemistry Department
- Radford University Planetarium
- Department of Physics
- Anthropological Sciences
- Selu Observatory
- Department of Information Technology
- Forensic Science Institute
- Geospatial Science
- MS in Data And Information Management
MS in DAIM Course Offerings
ITEC 541: Advanced Database Management Systems
Advanced topics and techniques in the modeling and manipulation of large data sets. Includes in-depth coverage of relational algebra and heavily nested SQL; physical database design and optimization; triggers and stored procedures; transaction control; assertions and other data integrity techniques; advanced modeling issues such as temporal design; and key topics in database administration. Students must implement one or more aspects of query execution and demonstrate an ability to implement scripts for common DBA (database administrator) tasks.
ITEC 542: Data Warehousing, Mining & Reporting
This course continues the study of principles of database systems, covering techniques for modeling, managing, and analyzing large data sets. The course provides an architectural roadmap for business intelligence with in-depth coverage of the dimensional model, data integration, data quality, reporting, data visualization, performance dashboards, machine learning algorithms, and the application of common data mining techniques. Students must implement a complete extract, transform, and load (ETL) process.
ITEC 641: Distributed Database Systems
A study of the issues surrounding distributed databases. Includes examination of techniques for data fragmentation and replication, distributed query processing, distributed transaction management, and distributed concurrency control. Introduces issues of scalability and related solutions including cloud computing and noSQL databases.
ITEC 643: Database Performance and Scalability
A detailed examination of database performance and scalability. Includes basic tuning of databases for transaction processing and data warehousing as well as techniques for load testing and load balancing of consolidated and distributed databases. Examines the performance of storage subsystems, computer clusters, and mainframe systems. Includes study of current industry tools and techniques for managing and processing big data.
ITEC 645: Information Security and Assurance
This course is an in-depth study of the reliability, security, and privacy issues in storage, transmission, and processing of large data sets. Key topics include security of database management systems and the infrastructure on which they execute; privacy issues; and mechanisms that support fault-tolerance and recovery of databases.
ITEC 647: Enterprise Information Architecture
Studies comprehensive, enterprise-wide approaches to organize, protect, and control trusted information from diverse sources as a strategic enterprise asset. The course covers information governance, master data management, metadata management, data quality and integration, and cloud computing architectures.
ITEC 660: Data Structures for DBMS
Study and application of advanced data structures and algorithms used in the storage, retrieval, and processing of medium to large data sets; study and application of current trends in algorithmic research in data and information processing.
ITEC 685: Information Analytics
Continues the study and application of data mining within the broader context of analytics covering structured, unstructured, and semi-structured data at rest and in motion. The course emphasizes big-data analytics examining data from social media, mobile devices and sensors, and other big-data sources; real-time processing of data streams; and architecture considerations including massively parallel processing technologies.