DST - Data Science and Technology

A study of the TCP/IP and Network Architecture. Students will learn how processors work. Coverage of network architectures as well as basics of computer  networks and the different protocol layers used for communication. Coverage of the concepts and fundamental principles that have contributed to modem network designs and implementation using TCP/IP. Topics to be addressed in this course are IP, ARP, RARP, and ICMP protocols; IP routing; TCP protocol, TCP/IP  next­ generation; OSI network protocols and standards; and client/server networking  and applications.  Prerequisite: ITS 20263 Introduction to Networking.

Principles of computer systems and network security.  Topics include network attacks and defenses, botuet, malware, social engineering attacks, privacy, and digital rights management.  Techniques for achieving security in multi-user computer systems and distributed computer  systems; cryptography: secret-key, public-key, digital signatures; authentication  and identification schemes; intrusion detection: viruses; firewalls; and risk assessment.  Prerequisite: ITS 20263 Introduction to Networking.

Understanding of Lean Six Sigma concepts and its methodologies with the goal of improved project management skills, problem solving, and more effective cross functionality.  Defining a problem and implementing solutions that are linked to the underlying root causes and delivering  improvements that are efficient, cost effective, consistent, reliable, and sustainable.   Prerequisite: MAT 20044 Introduction to Statistics or OAD 30763 Business Statistics.

This course introduces the major quantitative models designed for competitive, and system forecasting in today's complex and increasingly large data-gathering business environment. The course is useful for multiple disciplines, including marketing, finance, and health care. Topics include statistical quality control, exponential smoothing, and seasonally adjusted trend analysis. Emphasis is placed on a general understanding of theory, mechanics, application potential, available software packages, and templates.  Prerequisite: MAT 20044 Introduction to Statistics or OAD 30763 Business Statistics.

This course explores advanced topics in cyber security. Students will be exposed to a wide spectrum of security activities, methods, methodologies, and procedures with emphasis on practical aspects of Information Security.  Topics include security principles, threats, attacks, security models, security policies, an overview of authentication, encryption, and certifications, security detection, business risk analysis, protection of information assets, examination of pre- and post-incident procedures, and an overview of the information security evaluation.  Prerequisite: DST 20003 Network Security.

An exposure to visual representation methods and techniques that facilitate the understanding of complex data.  Students will be able to present a visual interpretation of data, and improve comprehension, communication, and decision making.  The course covers how the human visual system processes and perceives images, good design practices for visualization, how to use existing tools to make visualizations, collecting data from web sites with Python, and programming interactive web-based visualizations.  Prerequisites: ITS 16163- Introduction to Computer Programming

A study of detection techniques to fight cyber-crime.  This course combines business acumen and technology skills for recognizing and mitigating vulnerabilities. Students will address methods to properly conduct a computer and/or network forensics investigation including digital evidence collection and evaluation and legal issues involved in network forensics.  Technical topics covered include detailed analysis of hard disks, files systems (including FAT, NTFS and EXT) and removable storage media, mechanisms for detecting hidden information, and the hands-on use of powerful forensic analysis tools. Prerequisite: ITS 30044- Advanced Database Systems

A capstone experience that provides an opportunity for students to use a number of common statistical analysis models to large databases in health services research. Emphasizes a conceptual understanding of appropriate modeling techniques and the use of statistical software packages. The course focuses on the application of methods to health services research questions, with an emphasis on regression design and interpretation.  Prerequisites: DST 30000 Lean Six Sigma, DST 30006 Cyber Security, and DST 30009 Data Visualization.