Information Technology and Analytics MS - Curriculum

This course provides an introduction to the topic of information assurance as it pertains to an awareness of the risks inherent in protecting digital content in today’s networked computing environments. Topics in secure data and information access will be explored from the perspectives of software development, software implementation, data storage, and system administration and network communications. The application of computing technologies, procedures and policies and the activities necessary to detect, document, and counter unauthorized data and system access will be explored. Effective implementation will be discussed and include topics from other fields such as management science, security engineering and criminology. A broad understanding of this subject is important for computing students who are involved in the architecting and creation of information and will include current software exploitation issues and techniques for information assurance. Lec/Lab 3 (Spring).

This course covers the purpose, scope, capabilities, and processes used in data warehousing technologies for the management and analysis of data. Students will be introduced to the theory of data warehousing, dimensional data modeling, the extract/transform/load process, warehouse implementation, dimensional data analysis, and summary data management. The basics of data mining and importance of data security will also be discussed. Hands-on exercises include implementing a data warehouse. (Prerequisites: ISTE-608 or equivalent course.) Lec/Lab 3 (Fall, Spring).

Internet of Things (IoT) refers to physical and virtual objects that are connected to the Internet to provide intelligent services for energy management, logistics, retail, agriculture and many other domains. IoT leverages sensors, wireless communication, mobile devices, networking and cloud technologies to create many smart applications. In this course, the students learn about IoT design and development methodologies that enable the development of IoT applications. The students have hands-on opportunities to program and build IoT prototypes through lab assignments and a course project. The students should have some programming knowledge and required to purchase a IoT kit. (This course is restricted to students in INFOST-MS.) Lecture 3 (Spring).

IoT is simply interconnected devices that generate and exchange data from observations, facts, and other data, making it available to anyone. This includes devices that generate data from sensors, smart phones, appliances, and home network devices. IoT solutions are designed to make our knowledge of the world around us more aware and relevant, making it possible to get data about anything from anywhere at any time. This course teaches how IoT data could help and execute data driven operational and business decisions. The students learn how IoT analytics can create adaptive business and operational decisions in intelligent, effective and efficient ways. First, this course provides students with an understanding of different types of IoT data and the knowledge of how to handle the data relate to IoT. Then, the students learn how to create and setup a cloud analytic environment, exploring IoT data. The course also teaches how to apply analytics and statistics to extract value from the data. Lastly, the course explores different use-cases for IoT data. Purchasing a IoT kit is required. (This course is restricted to INFOST-MS or HUMCOMP-MS or DATASCI-MS students.) Lec/Lab 3 (Fall).

Information technology projects require the application of sound project management principles in order to be developed on time, on budget, and on specification. This course takes students through the nine knowledge areas of modern project management and the utilization of project management principles in both traditional and agile environments. Lecture 3 (Fall).

Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains. (Prerequisite: DSCI-633 or equivalent course.) Lec/Lab 3 (Fall, Summer).