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Course Criteria
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3.00 Credits
Spatial analysis often uses methods adapted from conventional analysis to address problems in which spatial location is the most important explanatory variable. This course is directed particularly to students with backgrounds in either computing or statistics, but who lack a background in the necessary geospatial concepts. Spatial Statistics with Geographic Information Systems will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
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3.00 Credits
This course covers advanced database management system design principles and techniques. Course topics include access methods, query processing and optimization, transaction processing, distributed databases, object-oriented and object-relational databases, semi-structured data, search engines, transaction management, advanced indexing, multidimensional data, performance evaluation, advanced structured query language (SQL), and database tuning. Students learn about the advanced object-relational features such as binary large objects (BLOBs), abstract data types, and methods. In addition, students learn about database programming including the use of triggers and stored procedures.
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3.00 Credits
Corporate data is used to make strategic business decisions. Business intelligence (BI) is used to present data in such a way as to help businesses survive in a competitive market. A data warehouse (DW) is a read-only analytical database that is used as the foundation for BI systems and is designed to consolidate data from the various data stores supported by an organization. This course will focus on the design and technical aspects of building a data warehouse. The topics covered will include the data warehousing life cycle, data warehouse architectures, system planning, warehouse requirements gathering, schema development, warehouse design, and other implementation issues.
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3.00 Credits
Data Analytics refers to the use of analysis techniques and processes to enhance productivity and business gains. Analytics is a multidimensional discipline that uses mathematics and statistics, descriptive techniques as well as predictive models to extract and present valuable knowledge from data stores. Organizations use analytics to process business data in an effort to describe, predict, and improve business performance. In this course, students will develop the knowledge and skills needed to extract and categorize data and to identify and analyze behavioral data and patterns using methodologies and techniques tailored to meet organizational requirements. Students will learn to apply frameworks for information optimization, visual analytics, data discovery, predictive analytics, and right-time and real-time analytics.
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3.00 Credits
This course explores the theories and methods used to search and retrieve text and bibliographic information from document repositories. Information retrieval focuses on the analysis of relevance and utility of information. The course will explore data organization and representation, and information access techniques; categorization, content analysis, data structures used for unstructured data, indexing and indexes, clustering and classification methodologies; search and navigation techniques; and search engines. In this course, students will learn to use statistical and linguistic methods for automatic indexing and classification, Boolean and probabilistic approaches to indexing, query formulation, and output ranking. In addition, students will learn to develop and analyze various data filtering methods, measures of retrieval effectiveness, and retrieval methodologies.
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3.00 Credits
This course will serve to introduce students to data mining and knowledge management. Data mining (DM) is concerned with the discovery of ""hidden"" knowledge in large data sets. This knowledge represents one aspect of an organization's intellectual capital and is often expressed in the form of trends or major themes that reoccur in the data. Knowledge management (KM) systems are designed to exploit the results of data mining and facilitate the analysis and evaluation of both tangible and intangible knowledge assets. In this course students will explore data mining methods used for prediction and knowledge discovery. These methods include regression, nearest neighbor, clustering, K-means, decision trees, association rules, and neural networks. In addition, students will become familiar with the current theories, practices, tools, and techniques used to management knowledge assets.
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3.00 Credits
In General Earth Science students will discover how science has investigated the origin, structure, and operation of planet Earth. Beginning with the Big Bang 13.7 billion years ago, the course will chronicle how the solar system and Earth were formed, the physical makeup of the planet, and the processes that result in its continuous transformation. Topics of particular emphasis will include the role and structure of the atmosphere, the movement of tectonic plates, earthquakes, volcanism, the formation and topography of ocean basins, the forms of erosion, and causes and impact of climate change.
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3.00 Credits
This course introduces students to a wide variety of basic atmospheric concepts and, in the process, enables them to gain a better understanding of ""how the weather works."" The course is best suited for students wishing to develop a fundamental understanding of Earth's atmospheric systems and to gain a greater appreciation for the atmosphere. The prime objective of this science course is to familiarize students to the physical processes associated with weather and climate, including jet streams, cyclones/hurricanes/typhoons, cloud types, air masses and fronts, global warming impacts, ozone hole El Niño/La Niña, Fujita scale, and anthropogenic climate change.
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3.00 Credits
Designed both for professionals working in the field and for students seeking a science elective, this course focuses on developing a scientific understanding of the causes and mechanisms of common natural disasters. The perspective is global and historical while focusing on contemporary events and potential for catastrophe. The emphasis of the course is on earthquakes, volcanic activities, flooding, and severe storms, and the consequent secondary disasters they can trigger. The course also addresses some of the social, economic, and political ramifications of these events.
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3.00 Credits
Economists divide their discipline into two areas of study: microeconomics and macroeconomics. While both areas are rooted in the actions of households and businesses, the distinction between microeconomics and macroeconomics is that the former focuses on individual households and individual businesses and the latter focuses on aggregates, namely aggregate households and aggregate businesses and how a country's economy works. This course introduces the student to the generally accepted principles of macroeconomics. It acquaints the beginning student with an appreciation of the functions of economic systems, including various approaches to the organization of production and allocation of resources, and of policies to achieve national economic goals. Specific topics include the determination of national income, inflation, recession, output, the business cycle, employment, unemployment, and interest rates. Students will discuss monetary and fiscal policies as well as international economic issues.
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