500

Overview of the field of data mining and analytics; large-scale file systems and Map-Reduce, measures of similarity, link analysis, frequent item sets, clustering, e-advertising as an application, recommendation systems.

Programming structures and algorithms for large-scale statistical data processing and visualization. Students will use commonly available data analysis software packages to apply concepts and skills to large data sets and will also develop their own code using an object­oriented programming language.

Development of web- and mobile-based front ends for large-scale data systems; with a focus of portability, accessibility, and intuitiveness.

This course will present key cryptologic terms, concepts, and principles. Traditional cryptographic and cryptanalytic techniques are covered plus perspective on successes and failures in cryptologic history, including both single-key algorithms and double-key algorithms. Issues in network communications, network security, and security throughout the different layers of the OSI model for data communications will also be discussed in depth, as well as the use of cryptologic protocols to provide a variety of security services in a networked environment. Authentication, access control, non­ repudiation, data integrity, and confidentiality issues will also be covered, plus key generation, control, distribution, and certification issues.

The theory and practice of visualizing large, complicated data sets to clarify areas of emphasis. Human factors best practices will be presented. Programming with advanced visualization frameworks and practices will be demonstrated and used in group programming projects.

The design and operation of large-scale, cloud-based systems for storing data. Topics include operating system virtualization, distributed network storage; distributed computing, cloud models (IAAS, PAAS, and SAAS), and techniques for securing cloud and virtual systems.

Algorithms for enabling artificial systems to learn from experience; supervised and unsupervised learning; clustering, reinforcement learning; control. Students will write programs that demonstrate machine learning techniques.

Expressing relationships among items in a way that enables automated, distributed analysis in an application-independent way; text mining to derive meaning from semantic networks; algorithms for processing semantic networks; developing a web of things.

Architecture and programming of parallel processing systems; distributed data storage techniques; multithreading and multitasking; redundancy; load balancing and management; distributed system event logging; programming techniques for maximizing the importance of distributed systems.

The capstone experience for students pursuing the Computer Science concentration in Data Science. Students will develop a solution for a real-world problem in data mining and analytics, document their work in a scholarly report, and present their methodology and results to faculty and peers.