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Analytics in Knowledge Management

Program of Study
Course Descriptions

Analytics in Knowledge Management (AKM) is the analytics of developing, organizing, retaining and using data resources that contribute to an organization’s sustained success. The knowledge-based economy and the explosive growth of analytics have increased the need for the creative and effective use of data. Notre Dame’s Master of Science in Analytics in Knowledge Management program responds to the growing demand for analytics professionals in today’s economy.

Both public and private organizations must now manage data from a variety of traditional and internet-based resources. To adapt and thrive in a fast-changing, competitive economic climate, organizations are hiring professionals who will enhance their ability to serve clients and realize strategic priorities. Notre Dame’s program meets the need for organizations to manage “big data.”

Tom Kuegler, General Partner at Wasabi Ventures LLC in Baltimore says “Analytics in Knowledge Management is a field of study that I see being used every day by our portfolio companies. Any company that targets the health care, legal, or educational markets needs to have an understanding of KM.”

Program of Study

Notre Dame’s Master of Science in Analytics in Knowledge Management is a multidisciplinary program of computer studies, mathematics and economics. The program focuses on competencies in areas of data management technologies, quantitative processes, and economic principles of change and risk management. It prepares professionals to specialize in the creation, enhancement and use of data through analytics.

The Analytics in Knowledge Management program includes 36 credits of coursework. Students can take two courses each semester, including summers, to finish in two years. The program builds skills and knowledge in areas that are crucial to potential employers.

Curriculum (36 Credits)

Computer Studies Courses (24 Credits)
CST-530 Foundations of Analytics in Knowledge Management (3)
CST-531 Data Design and Management (3)
CST-532 Knowledge Tools (3)
CST-540 Data Visualization (3)
CST-550 Project Management (3)
CST-610 Critical Inquiry (3)
CST-611 Data Security (3)
CST-620 Data Mining and Warehousing (3)

Mathematics Courses (6 Credits)
MAT-575 Applied Statistics (3)
MAT-576 Data and Decision Modeling (3)

Economics Courses (6 Credits)
ECO-550 Managerial Economics (3)
ECO-560 Risk Analysis (3)

Course Descriptions

CST-530 FOUNDATIONS OF Analytics in Knowledge Management
Introduces the data, information, knowledge, wisdom continuum. Topics covered include the historical roots of Analytics in Knowledge Management, theories/definitions of knowledge, and culture and strategies of Analytics in Knowledge Management. [3 credits]

Covers fundamental concepts for the design, use and implementation of database systems. Concepts include basic database modeling and design, query optimization, concurrency control, recovery and integrity. [3 credits.]

Studies concepts for SQL procedures, functions, packages and Internet database connectivity. Web application development techniques based on client and server-side programming are introduced. Standard methods and protocol for knowledge representation and exchange over the Internet such as XML, RDF, SOAP, WSDL and UDDI are discussed. [3 credits]

Explores the field of data visualization, including data types and visualization categories such as time-series, statistics, maps, hierarchies, and networks. Includes a study of visualization tools, infographics, and other issues related to the display of “big data.” Prerequisite: CST 531
[3 credits]

Reviews the application of project management tools as they apply to the systems development life cycle, including planning, organizational structure, and control mechanisms. Research assignments relate to the design and implementation of knowledge construction and management. [3 credits]

Studies the role of critical thinking, evaluation and research in information and knowledge work. The course will cover the steps in carrying out a research project: problem identification, theoretical framework, methodological design, data collection and analysis; developing a research proposal; communicating research results; and assessment and use of results of research studies. The course will also provide a critique and review of research studies, and discuss ethical concerns and issues associated with research. Prerequisite: MAT-575 [3 credits]

Provides an overview of both the theory of and applications for providing privacy, ethics and security in database management systems. Concepts include discretionary and mandatory access control, data integrity availability and performance, secure database design, data aggregation, data inference, secure concurrency control and secure transactions processing. Prerequisite: CST-531[3 credits]

Provides an overview of the data mining and warehousing components of the knowledge discovery process. Data mining applications are introduced, and the application of statistical algorithms and techniques useful for solving problems are identified. Students will study development issues such as identification, selection, acquisition, processing, search and retrieval. [3 credits]

Introduces economic methodologies to managerial decisions. Examines consumer demand, production costs, and output/price combinations that maximize firms' goals under different market structures. Applies basic math and statistics tools to evaluate business choices. All statistics and mathematics used in the class are explained in basic terms at the point of first usage. [3 credits]

Covers risk analysis as an evolving paradigm for decision making in uncertain situations. Risk analysis consists of three tasks: risk management, risk assessment and risk communication. This course introduces the language, models and methodologies of risk management, assessment and communication with an emphasis on the need for addressing uncertainty in all phases of decision making. [3 credits]

Covers concepts of testing for use in professional sciences, including simple linear regression, correlation, multiple regression, fixed and random effects, analysis of variance, analysis of covariance, experimental design, multivariate methods and various statistical packages. Prerequisite: MAT-576 [3 credits]

Provides an application-oriented introduction to the modeling techniques used to structure the way we think about managerial decision situations. Methodologies considered include decision analysis, simulation, optimization and sensitivity analysis. Stochastic models are developed with applications to finance, operations management, logistics and resource allocation. Prerequisite: CST 531 [3 credits]