Artis College of Science and Technology
- Davis College of Business and Education
- 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
- Medical Laboratory Science
- Museum of the Earth Sciences
- Mathematics and Statistics
- Chemistry Department
- Radford University Planetarium
- Department of Physics
- Anthropological Sciences
- Selu Observatory
- Center for Information Security
- REALISE Students
- Forensic Science Institute
- Biomedical Science
- Geospatial Science
- School of Computing and Information Sciences
- MS in Data And Information Management
The Concentration in Database at Radford University
Information is power. As a result, ensuring the integrity, security, and availability of data is a critical task within organizations. The Database concentration educates students in all aspects of data management from database design to database administration to data warehousing and analysis. The concentration also provides an excellent foundation from which to pursue a career in the design and implementation of database management system (DMBS) software.
Talented database engineers are challenging to find which creates high demand and high salaries. Radford University offers the only undergraduate database program in the state of Virginia. Students who complete the database concentration work with Oracle, the industry leading database management system (DBMS), for at least three semesters. The Database concentration prepares students for careers as database developers, database administrators, and data analysts and data scientists.
Database developers fall into two categories: software engineers and data developers. Software engineers design and develop applications and systems including mobile apps, web apps, and any other kind of software. Most, if not all, business critical systems are information systems that operate on data. Consequently, database skills make software engineers far more valuable. Some database developers work entirely in the database modeling data and writing code that executes in the database management system.
Data developers are programmers who process data. Raw data is dirty and messy. Raw data is often filtered to remove the chaff much like pay dirt is filtered to separate out the gold. The important data must be cleaned and organized to produce valuable information assets. Clean data typically needs to be prepared for different applications like data mining and analytics.
Database administrators (DBA) manage the database system. Data is one of the most critical resources for any organization. The DBA’s top priority is to keep the database up and running to ensure the data is always available. Data must be protected from unauthorized use. The DBA designs and implements access controls to secure the data. DBAs install software, patches, and upgrades. They manage backup and recovery to ensure data is never lost and they tune performance to maximize throughput.
Data analysts and data scientists are the rock stars of Information Technology. They understand data from a business perspective and help people and organizations make better decisions.
Data analysts are typically embedded in a functional unit (e.g., marketing) to answer business questions with data. Analysts produce reports and dashboards, often applying data visualization techniques, enabling business users to identify problems, recognize opportunities, and increase performance. Analysts improve business intelligence by describing what is happening now.
Data scientists try to predict what is going to happen. They work with the data team applying machine learning and deep learning to develop predictive and prescriptive data models. Data scientists typically have strong math skills and they use tools like R, Python, and Scala.
Machine learning uses historical data to predict the future. For example, predict which customers are most likely to churn (choose another service provider), identify customers most likely to respond to a marketing campaign, and improve customer service (e.g., predict customer volume at a grocery store and staff accordingly). Predictive analytics requires people to make decisions. Prescriptive analytics recommends actions to take. Many recommendations are being automated to increase response rate by removing people from the loop. Self-driving cars analyze the environment and determine the best direction to take based on data.
- Class Completion Flowchart 2022-2023
- Class Completion Flowchart 2021-2022
- Class Completion Flowchart 2020-2021
- Class Completion Flowchart 2019-2020
- Class Completion Flowchart 2018-2019
- Class Completion Flowchart 2017-2018
- Class Completion Flowchart 2016-2017
- Class Completion Flowchart 2015-2016
- Class Completion Flowchart 2014-2015
- Class Completion Flowchart 2013-2014
- Class Completion Flowchart 2012-2013
- Class Completion Flowchart 2011-2012
- Progress Sheet (PDF)
For more information on this challenging but highly rewarding major contact: Dr. Arthur Carter
School of Computing and Information Sciences
Box 6933 Radford University
Radford, VA 24142