Master of Science in Computer Information Science with a Concentration in Data Science Online

This course addresses the design and development of standards-based client interfaces for Web applications. The course includes Web-based standards and toolsets that support these standards. Application development emphasizes client Web interface scripting to serve as a general introduction to computer programming. The specific toolset used will depend on the types of interfaces to be developed, considering technology trends. Examples of possible tools include XHTML, CSS, and JavaScript. This course may be waived if the student has prior experience in client interface development.

This course focuses on the development of Web services for use by many different types of Web applications. The course develops basic programming techniques to implement the server side function of the application. The course uses a non-Windows interface for the tools set.

This course encompasses programming models that support database access, including ADO.NET. It covers client/server and multitiered architectures; development of database applications; Internet and intranet database design and implementation; database-driven Web sites; and use of XML syntax related to databases. Examples of the possible tool sets for this tool set are PHP and mySQL on either a Linux or Windows server. The course also considers privacy of data and data protection on servers.

This course covers development of mobile applications and integration with existing systems on the devices. Students will extend development of mobile solutions with enhancements to views, layouts, and intents including interaction with the location-based services, messaging services, multimedia interfaces, and sensors available on the mobile device. The applications will manage data sources, both locally and from database providers. The applications will be tested in an emulation environment and prepared for deployment in a mobile marketplace.

Special Topics as assigned by student/faculty.

Students culminate their learning with a capstone project under the supervision of a faculty advisor. Some students partner with an external company or work on a project associated with their employer as a project deliverable for that company. Prerequisite: All Core courses.

This course entails analysis and evaluation of database designs in relation to the strategic mission of the project. Topics include database systems, database architectures, and data-definition and data-manipulation languages. Also included are logical and physical database design, database models (e.g., entity-relationship, relational), normalization, integrity, query languages including SQL, and relational algebra, in addition to social and ethical considerations and privacy of data. This course incorporates case studies and a project using a relational DBMS.

This course introduces the field of data mining, with specific emphasis on its use for Machine Learning algorithms. Techniques covered may include conceptual clustering, learning decision rules and decision trees, case-based reasoning, Bayesian analysis, genetic algorithms, and neural networks. The course covers data preparation and analysis of results. Skills in Microsoft Excel are useful.

This course introduces students to the field of artificial intelligence (AI). Students will learn how big data and data mining techniques are utilized by machines to create the AI models used by autonomous aircraft and automobiles, personal assistants, IT security software, fraud investigations and credit bureaus. The course will review the history, present day use, and future of artificial intelligence. Through case studies and current events, students will examine the benefits and risks associated with AI. The course will cover issues related to AI and privacy, ethics, and machine bias. Neuromorphic computing, the Open Neural Network Exchange (ONNX), and data analytics will also be discussed.

This course will require students to learn the R programming language and assess how to use it and find interesting features in data. Students will learn about R and statistical best practices and how to display data in a manner that will help you explain your findings to those who do not have a technical background. Moreover, the course introduces students to modeling and simulation. Topics may include basic queueing theory, the role of random numbers in simulations, and the identification of input probability distributions.

This course introduces students to mathematical models that can be employed to make informed decisions in a wide variety of data-driven fields, including (but not limited to) finance, banking, marketing, health care, retail, manufacturing, and transportation. Goals such as increasing revenue, decreasing costs, and improving overall efficiency of operations in the face of various constraints are considered. Students learn to recognize when a problem lends itself to a particular type of model, formulate the model, and use appropriate methods to solve or extract information from the model. Particular emphasis is placed on linear programming (with exposure to network models and integer programs) and the simplex method. Forecasting, inventory management, and queueing models, as well as Markov chains, are also studied. Additional topics covered include sensitivity analysis, duality, decision analysis, and dynamic programming. Software (both spreadsheets and a computer algebra system) is employed consistently throughout the course to expedite the solution and analysis process; emphasis will be placed on the practical application of models rather than on the models' mathematical properties.