Full Course Description List:

MDSC Courses

5 credits

Students in this course are provided a critical "state-of-the-art" breadth-first review of the domain of information technology (IT). Designed to provide students with a wide-ranging introduction to topics such as software engineering, cybersecurity, and big data analytics, students completing the course have a comprehensive global view of the current IT landscape in the context of both commercial and noncommercial enterprises. The class blends both theory and practice to provide a solid foundation for future study. Students study the relationship between technological change, society and the law, and the powerful role that computers and computer professionals play in a technological society.

5 credits

Students in this course are introduced to the concepts of artificial intelligence and emergent areas of intelligent systems. Students have the opportunity to gain a critical understanding of knowledge representation, reasoning, machine learning, and evolutionary techniques. Students are presented with real-world problems and have the opportunity to apply "intelligent" techniques to provide solutions to these problems.

Prerequisites

• MDSC 6005

5 credits

Cloud computing has attained great commercial significance in recent years. As companies seek to drive down the capital (and recurrent) costs of doing business, using cloud computing to reduce expenditure by outsourcing aspects of the organizations' IT infrastructure to external, web-accessible systems has become a critical goal. In this course, students study the key concepts, theories, techniques, and practices that underpin cloud computing, including the main abstraction, component and deployment models that characterize cloud computing. Students have the opportunity to critically appreciate issues and problems, as well as cutting-edge solutions, pertaining to cloud computing.

Prerequisites

• MDSC 6005

5 credits

In order to create a competitive advantage, organizations store and analyze information in a variety of formats. This course covers key areas of database systems, such as requirements, design, implementation, security, performance, and scalability. Through a hands-on approach and practical projects, students have an opportunity to design and build database systems using the latest database technologies.

Prerequisites

• MDSC 6005

5 credits

Students in this course are provided with a comprehensive understanding of big data tools and techniques, related issues, and the different kinds of big data ecosystems that can be used to support advanced data analytics. Students consider big data management frameworks in general, but with a focus on the Hadoop open source distributed data storage and processing platform and its underpinning subsystems. Additionally, the course content introduces students to the role of big data systems in data-driven decision-making.

Prerequisites

• MDSC 6005

5 credits

In this course, students are provided with an in-depth understanding of the concepts of data mining, including the end-to-end processes involved and the major data mining tools and techniques in common usage. During the course, students have the opportunity to apply such tools and techniques to a variety of example data sets in order to gain a critical insight into their operation and an understanding of when and where such tools and techniques can best be applied. Students also have the opportunity, using the 'R' programming language, to implement several different kinds of data mining algorithms to gain a comprehensive understanding of their operation.

Prerequisites

• MDSC 6005

5 credits

Statistical analysis supports quality management, drives decision making, enables forecasting and prediction, and provides a means for understanding many aspects of our world. Data is everywhere in today's integrated technological society, and statistical analysis provides the means to access and interpret data. Students in this course are introduced to statistics focused on working with complex data sets and analyzing big data. Students synthesize theory with practical applications to learn the fundamentals of statistical reasoning, descriptive statistics, visual data display, regression, hypothesis testing, research design, anomaly detection, and advanced analysis practices. They have the opportunity to practice using a statistical software package to solve statistical problems. Students use a publicly available big data set to formulate their own study and complete a statistical analysis.

Prerequisites

• MDSC 6005

5 credits

Students in this course are provided with insight into how predictive analytics can be used within organizations. In completing this course, students have the opportunity to gain a comprehensive understanding of how results from predictive analytics can be used by organizations to grow their customer base and run operations more efficiently. This course is oriented toward the practical applications of predictive analytics.

Prerequisites

• MDSC 6005

5 credits

Big data normally refers to petabytes (1000 terabytes) or exabytes (one billion gigabytes) of unstructured data. This amount of data requires new methods to analyze, visualize, and present these data in a way that yields insight and understanding. Students in this course are introduced to elementary graphics programming, focusing primarily on two-dimensional vector graphics and the programming platforms for graphics. This infrastructure will also include lessons on the human side of visualization, studying human perception and cognition to gain a better understanding of the target of the data visualization.

Prerequisites

• MDSC 6005