Graduate education encourages the development of advanced skills, theoretical knowledge and critical thinking skills to practice the art and science of cybersecurity techniques to defend networks and systems, analyze attacks, and conduct forensic evaluations.
Students entering the cybersecurity program must have an undergraduate degree in Computer Science, Management Information Systems, Information Technology, or a related degree with an understanding of basic software programming, network management, and systems/development operations. Professional experience may be accepted upon evaluation. Additionally, it is important for the student to be proficient in written and oral communication skills.
The master of science (MS) in cybersecurity with emphasis in data analytics prepares individuals for demanding positions in public and private sectors analyzing, managing, operating, or protecting critical computer systems, information, networks, infrastructures and communications networks.
Students will be well-versed to apply their knowledge and critical thinking related to social engineering techniques, network defenses, online malware methods, critical infrastructure protection, fraud, theft, digital forensics, and threat detection.
The data analytics emphasis focuses on developing and applying data analytics skills to fulfill significant needs in the business community. Students will integrate business concepts as well as key methods and tools for large-size data modeling, analysis and solving challenging problems involving "Big Data."
Learning Outcomes
Data Analytics emphasis:
- Explain the role of data analytics in organizational decision making.
- Compose query statements to implement the data definition and manipulation, and construct multidimensional data cubes analysis.
- Apply effective methods for analyzing, presenting and using informational data.
- Develop meaningful reports and visualization of business data analytics appropriate to a technical and non-technical audience.
- Articulate forecasting and predictive models for real-world analytical applications.