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Elective Courses

Your four electives can be used to create a specialization in marketing, health care or supply chain, or to develop a broad-based understanding of analytics.

Data Analytics and Visualization (SEIS 632)

The course provides an introduction to concepts and techniques used in the field of data analytics and visualization. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery and best practices for creating effective data visualizations. Through practical application of the above topics, you will also develop proficiency in using analytics tools.

Supply Chain Analytics (OPMT 729)

The aim of this course is to provide a foundation in the use of a variety of analytical tools for managerial decision making in a supply chain context. The course covers two separate areas of analytics. The first is statistical tools and the second is mathematical modeling and optimization. Both are important areas that are increasingly being utilized in business decision making at all levels. The emphasis of the course will be on the understanding and application of these techniques and tools for decision making, and not on the mathematics of these techniques.

Interactive Marketing (MKTG 775)

This course examines the concepts, strategies and applications involved in interactive marketing. Interactive marketing is characterized by activities that address customers directly for the purposes of initiating an exchange as well as developing, managing and exploiting a customer relationship. Interactive marketing encompasses aspects of direct mail, database marketing, customer relationship management and Internet marketing.

IT and Business Analytics (OPMT 650)

This course provides a comprehensive overview of the information technology used by firms for coordinating various functions within a firm, as well as for connecting and collaborating with their suppliers and customers. It focuses on issues related to collection, storage, analysis and presentation of data with significant attention to ethical use of information technology. The course takes a strategic approach to managing information technology for gaining competitive advantage, and no prior background in information technology is necessary.

Health Care Analytics (SEIS 735)

This course will discuss processes in health care analytics, including data acquisition, storage, retrieval, management and analysis of health care data in heterogeneous formats (i.e. numeric health records, medical text and medical images). Major topics include: (1) analyzing patient records and identifying frequent medical sequences for treatment and prevention; (2) evaluating medical text and generating aggregated summary based on hierarchical medical concepts; (3) retrieving information from different types of medical images; (4) building clinic decision support systems to detect possible medical mistakes; and (5) comparing brain connectivity graphs from patients with different neurological conditions. Amazon Cloud will be used to analyze multi-million records of numeric and text data.

Data Warehousing and Business Intelligence (SEIS 732)

Prerequisite SEIS630

In order to build and maintain a successful data warehouse, it is important to understand all of its components and how they fit together. This course will cover data warehouse and data mart lifecycle phases while focusing on infrastructure, design and management issues. The course project will provide an opportunity to for hands-on experience with some of the available tools and technologies.

Topics include: differences between data warehouses and traditional database systems (OLTP), multidimensional analysis and design, building data warehouses using "cube" vs. RDBMS (Star schema, etc.), planning for data warehouses, extraction transformation and loading (ETL), online analytical processing (OLAP), data mining, quality and cleansing, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, and the impact of new technologies (data webhouse, clickstream, XML).

Quality Management including Six Sigma (OPMT 630)

This course provides an introduction to the principles and practices of quality management. It covers basic tools and techniques of quality, but will focus on the managerial application of those tools and techniques. Modern approaches to quality management such as the Baldrige criteria, ISO certification and Six-Sigma programs will be included, as well as the philosophies of quality pioneers such as Deming and Juran.

Process Analysis, Lean and Agile Organizations (OPMT 635)

The primary objective of this course is to learn and apply the concepts and techniques of business process analysis and improvement. Students will learn how to analyze and improve business processes in different contexts using appropriate different process improvement tools. Fundamental concepts that can be used to systematically analyze any business process will be covered, as well as more focused programmatic techniques such as lean/agile/flexible (and/or JIT) systems, Theory of Constraints (TOC) and Business Process Reengineering.

Health Care Systems: Overview (MGMT 630)

This course provides you with an accurate understanding of the various components of the health care system – providers, consumers, payers and third-parties – and how they relate. You will learn about issues, motivations and incentives that influence all parts of the system, and gain an understanding of the political and social environments in which they operate.

The Creative Process (BCOM 620)

Explore the literature of creativity, the study of creative persons and their contributions to society, and the process by which creative ideas are produced and communicated. Active participation in strategies for actualizing the creative potential of individuals and groups is an essential part of the course.

Data Mining and Pred. Analytics (SEIS 734)

To overcome data overloading problems, this course will discuss how to apply big data analytics to extract useful patterns from huge datasets and generate visual summary of data. This course will also demonstrate mining and analyzing big data on Amazon Cloud.

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