The Data Science Analysis pathway focuses on advanced knowledge of data science techniques and theories. Students with backgrounds in computing will have the opportunity to explore cutting-edge Information Systems topics that are relevant to Data Science.
Admission into the Data Science graduate program
Background in computing
Select three courses to fulfill the pathway requirement
IS 661: Biomedical Informatics Applications
The focus of this course is on advanced topics in healthcare information systems. Examples of topics include the implications of the administrative simplification provisions (e-commerce standards, privacy and security) of the Health Insurance Portability and Accountability Act, the workflow management aspects of cancer center information systems and information retrieval aspects of cancer research libraries.
Prerequisite: IS 660.
IS 706: Interfaces For Info. Visualization & Retrieval
Providing access to large amounts of information is an important function of information systems. This course discusses the designs of user interfaces that allow users to search for, browse and interact with information. Specifically, students will be introduced to human information-seeking behavior and its implications for user interfaces, including user interfaces for information retrieval systems and a wide variety of information visualization tools. Information retrieval systems enable users to search for and browse information. Information visualization is the application of computer-supported graphical tools to presentation of large amounts of abstract information.
Prerequisite: IS 629 or consent of the instructor.
IS 707: Applications of Intelligent Technologies
This course provides a survey of artificial intelligence concepts, technologies, applications, techniques, methodologies and issues. The first half of the course will focus on expert systems and the knowledge engineering life cycle. The second half of the course will highlight various knowledge technologies, including case-based reasoning, genetic algorithms, fuzzy logic, neural networks, hybrid intelligent systems, data mining and knowledge management. The course also will discuss management implications of use, non-use and misuse of AI technologies.
Prerequisites: Graduate student standing and consent of the instructor.
IS 721: Semi-Structured Data Management
This course offers understanding of the latest technologies to manage semi-structured data such as XML and provides hands-on experience on managing and querying semi-structured data using relational database management systems. This course also introduces students to two important application areas of semi-structured data: data sharing and data privacy. Topics include, but are not limited to basic concepts of XML, XML Schema (XSD), XML query languages such as XPath amd XQuery, storing XML in databases, querying XML in databases, publishing XML from databases, privacy issues for data sharing, solutions to privacy issues including Platform for Privacy Preferences and XML encryptions, privacy preserving data mining, and economis aspects of data privacy. Students will keep abreast of the latest technologies and research innovations in the field of semi-structured data management, data sharing, and data privacy. There will be database programming assignments to familiarize students with the course topics. In addition, a group project will be part of the course to expose students to real life application of semi-structured data management technologies.
IS 722: Systems and Information Integration
This course focuses on the theory and practice of integrating systems and information. The problem of integrating information is extremely common nowadays when an organization buys another and inherits an entire IT department which may not be compatible with its own one. Data systems and information should easily interoperate for the success of the organization. This course investigates the various technologies in the field of information integration with an emphasis on semantics. Topics that are covered include: Data Integration Architectures, Data Warehouses, Modeling Data Semantics, Semantic Interoperability, Metadata, Semantic Integration Patterns, Context-Awareness, Semantic Networks, Mediation and Wrapper techniques, Web Services and Service Oriented Architectures (SOA), Integration Servers, etc.
Prerequisite: IS 620
IS 728: Online Communities
Social interaction via the Internet is becoming increasingly important. People are gathering in online communities of interest and communities of practice to discuss health, hobbies, games, education, politics and professional issues. In this class, students will analyze the technology and social support needed to make these social interactions successful. They also will discuss and debate current research in this field and either develop an online community or carry out a small research project.
IS 731: Electronic Commerce
This course analyzes the role of Web design in electronic commerce (e-commerce) from organizational and operational perspectives, and is focused on user-related (front-end) issues in e-commerce. One of the goals of Human-Computer Interaction (HCI) is to solve real-world problems in design and use of technology in the e-commerce environment from the user’s perspective. Tools and techniques for creating and improving e-commerce sites are emphasized, as well as developing guidelines, heuristics and testing methods. Structure, navigation and information sharing-related HCI issues are covered within the context of e-commerce.
IS 733: Data Mining
The purpose of this course is to provide a comprehensive discussion of using organizational databases to enable decision support through mining data. This course will provide an in-depth understanding of the technical, business and research issues in the area of data mining. Areas of data mining will include justifying the need for knowledge recovery in databases and data mining methods such as clustering, classification, Bayesian networks, association rules and visualization. The course will provide a brief introduction to issues in data warehousing which include designing multi-dimensional data models; cleansing and loading of data; reporting; ad hoc querying and multi-dimensional operations, such as slicing, dicing, pivoting, drill-down and roll-up operations. New areas of research and development in data mining will also be discussed.
Prerequisite: IS 620.
IS 777: Data Analytics for Statistical Learning
IS 777 has the objective of introducing students to the essential concepts related to analyzing data for statistical learning. The fundamental building blocks, principles, and ideas related to analyzing data and building statistical models will be discussed. Furthermore, the course will involve application of the concepts by including various assignments, exercises, and activities in the statistical environment, R. In addition, the teams of students will be engaged in a semester-long project involving a particular topic selected by students.
Choose from other available electives to complete the requirements for this program
Data analysis is a quickly-growing field and demand for experienced data science professionals will only continue to grow. According to the Bureau of Labor Statistics, data analyst jobs are expected to increase by nearly 20% between 2014-2024 and the Baltimore-Washington metro area has seen a disproportionate rise in the number of employers seeking candidates with expertise in data science.
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