Information Technology and Analytics MS - Curriculum

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).

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).

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).

This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics. Lec/Lab 3 (Spring).

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).

In this course, students will build applications that interact with databases. Through programming exercises, students will work with multiple databases and programmatically invoke the advanced database processing operations that are integral to contemporary computing applications. Students will examine and evaluate alternative approaches for each of these operations. Topics include the database drivers, the data layer, connectivity operations, security and integrity, and controlling database access. (Prerequisites: ISTE-608 or equivalent course.) Lec/Lab 3 (Fall).

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).

Students will be introduced to issues in client/server database implementation and administration. Students will configure, test, and establish client-server communication and server-server communication with single and multiple database servers. Topics such as schema implementation, storage allocation and management, user creation and access security, backup and recovery, and performance measurement and enhancement will be presented in lecture and experienced in a laboratory environment. Students will configure and demonstrate successful communication between a database file server and multiple clients. (Prerequisites: ISTE-608 or equivalent course.) Lec/Lab 3 (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).

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).

This course supports the proposal development process for graduate students enrolled in the MS in information sciences and technologies, the MS in networking and system administration, or the MS in human-computer interaction program who are beginning the project or thesis experience and require additional structure and support. Students begin the development of an acceptable proposal and through weekly meetings students are guided toward the completion of the proposal, which is a prerequisite for formal thesis or project registration. Note: Students must have completed all their course work prior to enrollment which is by permission of the graduate program director. Lecture 1 (Fall, Spring).