Computer Science (Master's)

Along with the rise of Internet commerce and social networks comes the opportunities and challenges of extremely large data sets where vital information is extracted by data mining. This course introduces the background, algorithms, and techniques for data mining specially targeting very large data sets. It begins with an introduction to data mining critical concepts. It then expands to the discussion of the map-reduce frameworks for parallelizing algorithms, which is the key for massive data set mining. The algorithms for locality sensitive hashing and streaming data mining will be followed. The course will then cover the techniques to find frequent item sets and clustering. Upon completion of the course, the student will have a solid foundation on how to efficiently and effectively extract information from massive data sets from myriad sources.

Web Development covers the fundamentals of web development from basic web structures to more advanced web page and web site development. This course views web development as both a science and an art. HTML 5 (Structure), CSS 3 (Presentation), and JavaScript (Behavior) will be introduced as the three foundation languages that form the basic structure of a web page. Communication protocols will also be discussed. The course material will cover security and how security can be built into web pages from the very start. The course has multiple projects which culminate with a fully developed web site that is both aesthetic and functional. (Prerequisites: None)

This course covers advanced software engineering techniques that facilitate human-centered design principles. Topics include product wireframes, user journeys, rapid prototyping, and running low-fidelity A/B tests. Software interfaces for science, technology, business, and medical devices will be developed using sound Human Computer Interaction techniques. A special emphasis will be on developing software interfaces for the disabled. The course culminates with the development of a professional level software interface design.

(Prerequisite: MSCS 2101 Software Engineering)

This course covers the foundations of ethnography and its application to the presentation of computer interfaces. Ethnography is a research method central to knowing the world from the standpoint of its social relations. It is a qualitative research method predicated on the diversity of culture. With ethnographic research, a strong emphasis is on exploring the nature of a particular phenomenon, particularly social based, rather than setting out to test hypotheses about it. With ethnography there is a tendency to work primarily with unstructured data, which are data that have not been coded at the point of data collection as a closed set of specific categories. This course uses ethnography as a foundation to develop socially and culturally acceptable and appropriate user interfaces. (Prerequisites: None)

This course introduces advanced data visualization techniques and provides advanced and more complex means to communicate information and tell a story of quantitative data through graphic patterns. It leverages big data techniques to improve the decision-making process in fields such as science, technology, engineering, and mathematics. The course presents cutting-edge tools and techniques to improve the analysis of information through scientific, visual processes and techniques. The course includes myriad case studies in the science, technology, engineering, and mathematics disciplines and industries.

(Prerequisite: MSCS 3019 Data Visualization)

This course expands the coverage of data science into strategic modeling for the effective and efficient study, analysis, and presentation of data. Models and data programming is accomplished in the Python and R languages. The course covers areas such as conceptual, enterprise, logical and physical data modeling as well as generic and semantic data modeling. Various modeling processes and methodologies will be covered as well as many of the tools used by data scientists to construct viable data models. The course will conclude with the development of an enterprise level scientific or engineering data modeling project. 

This course covers emerging advanced topics in the field of data science. It surveys cutting edge research as well as tools and techniques that are expanding the domain of data science including areas of science, medicine, quantum computing, and business. The course includes extensive readings in data science and its cutting edge application and requires the student to perform their own research into the domain and to produce relevant research papers.

This class is a guide through a professional internship to ensure that the internship provides professional learning experiences that will be applied to the professional arena. Students undertake a significant experiential learning opportunity, typically with a company or community-based organization. The internship represents an educational strategy that links classroom learning and student interest with the acquisition of knowledge in an applied work setting. Through direct observation, reflection and evaluation, students gain an understanding of the internship site's work, mission, and audience, how these potentially relate to their academic study, as well as the organization's position in the broader industry or field. Students will produce a critical reflection on their internship experience demonstrating how they have addressed specific learning goals.