Course Catalog

Required Courses (8):

BAN 500: Descriptive Analytics (3) – This course covers statistical inference as applied to management decision making and focuses on building linear statistical models and developing skills for implementing statistical analysis in real situations.  Applications require the use of statistical analysis programs on the computer.

BAN 501: Prescriptive Analytics (3) - This course introduces students to the field of prescriptive analytics.  Students will learn how to develop and use modeling techniques used extensively in the business world. Both mathematical and spreadsheet skills in MS Excel are utilized for performing optimization, simulation, and decision analysis techniques.

BAN 502: Predictive Analytics (3) - This course explores computer-intensive methods for model selection, parameter estimation, and validation for predictive analytics. The course focuses on techniques and algorithms from the statistical and machine learning disciplines and has a strong programming component. Example topics in this course include ordinary least squares regression, logistic regression, multinomial logistic regression, classification and regression trees, neural networks, support vector machines, naïve Bayes, principal components analysis, cluster analysis, and regularization. Each technique is accompanied with a focus on application and problem-solving.

BAN 525: Case Studies in Business Analytics (3) - This course consists of case studies on using statistical methods for prediction in some business settings. The statistical methods used in the cases follow the recent advances in forecasting with big data and include methods such as decision trees and other classification models, neural networks and stepwise regressions. The JMP Pro software, which is available at UNCW, will be utilized in all cases. The course will also include a data intensive project, which will ask students to use the approaches introduced in the cases to forecast the US economy or the stock market.

BAN 530: Applications of Business Analytics (3) - This course requires students to apply theoretical and practical knowledge acquired during the Business Analytics program at a comprehensive capstone project. Students will develop a solution to a real-world problem that will include collecting and cleaning data, building an appropriate model, and using appropriate analytic methods to solve the problem. The course instructor will provide a problem statement and data from either a real-world domain or a close approximation. Students will work individually or in small teams to develop a project plan, appropriate models, and a final recommendation. It may be possible for students to propose their problem statement and data collection, however, all such projects must be instructor approved.

MIS 503: Programming for Analytics (3) - This course introduces the essential general programming concepts and techniques to a data analytics audience with limited or no prior programming experience. Students will learn programming foundations, application development in R and Python, and how to integrate applications with business operations in this class. The course covers hands-on issues in programming for analytics, which includes accessing data, manipulate data objects, analyze data using common statistical methods, generate reproducible statistical reports, and creating informative data graphics. The course introduces software techniques to write functions, debug, and organize and comment code.

MIS 504: Database for Analytics (3) –  This course focuses on the conceptual foundations of relational databases and data management, interpreting database structure for relevant data, data queries and reporting, and searching for data anomalies [sometimes referred to as “data cleansing” of errors and inconsistencies].  Students will become familiar with database modeling and logical design.  Proficiency in developing complex queries and report generation is stressed.

MIS 505: Data Visualization (3) - This course provides experience in design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.  Use of popular and powerful data visualization and dashboarding software tools is emphasized.

Electives (Select TWO):

MIS 506: Text and Unstructured Data Analytics (3) - This course addresses the unstructured data management skills needed for modern data analysis including those salient to big data and real-time data environments. The focus is on unstructured data and its environment. Unstructured data includes web data (blogs, text), user generated content, social media, location-aware data, and digital media among others. Topics covered include extraction methods for real time audio and video data, data capture, cleaning, representation, storage, queries, manipulation, and real-time data management. Also included as they apply to unstructured data environment are data security, governance, and visualization. Students will learn natural language processing and geospatial analytical tools.

MIS 527 - Data Center Management (3) - Fundamentals of Data Center technologies and management. Students learn the roles of databases, computing hosts, connectivity, and storage in a data center. The details of storage technologies, storage network protocols and computing architectures are covered. Management and design considerations such as virtualization of resources, metered usage, business continuity, recovery, replication, and security are also discussed.

MIS 592 - Topics in Computing (1-6) - Course Description: Topics in computing of current interest not covered in existing courses.

BAN 513: Strategic Marketing Analytics (3) - The focus of this course is to familiarize students with the principles and strategic concepts of marketing analytics, a high growth area that uses computer-based analytical techniques and quantitative modeling to enhance decision-making capabilities of marketing managers. The effective use of marketing analytics offers insights into customer preferences and trends and allows for the detection of patterns, the making of new associations, and the acquisition of a deeper understanding of customers.

BAN 515: Health Care Analytics (3) - This course equips students with health analytics skills to select, prepare, analyze, interpret, evaluate, and present clinical and operational data to improve healthcare outcomes.  Theoretical and practical coverage of topics is presented, such as data mining, predictive modeling, association analysis, clustering, and visualization.  

BAN 516: Transportation Analytics (3) - This course explores various statistical analysis techniques used in transportation systems.  Various practical transportation topics will be covered, including model estimation, data analysis, traffic forecasting, incident prediction, traffic flow theory, and safety.  Techniques to be covered include statistics, data mining, hypothesis testing, experimental design, and optimization, such as regression, time series modeling, classification, and clustering.  Popular statistical modeling software will be used to assess transportation solutions.

BAN 517: Supply Chain Analytics (3) - This course introduces the application of data analytics in various aspects of supply chain management, including forecasting and inventory management, sales and operations planning, transportation, logistics and distribution, purchasing, and supply chain risk management.  Software packages include Excel with Solver and R.

BAN 592: Current Topics in Business Analytics (3) - This course introduces students to a current area of application or a current development of an advanced topic in business analytics.  Students will learn how Business Analytics solutions can impact a firm’s ability to create and maintain competitive advantages. Topics may include applications or developments in descriptive, predictive, or prescriptive analytics. 

BAN 598: Internship (3)

SCM 572: Project Management (2-3) - This course introduces the problems of managing a project with the purpose of achieving a specific objective. There will be an in-depth coverage of the operational and conceptual issues faced by modern project managers in all organizational settings. Students will learn techniques, terms and guidelines that are used to manage costs, schedules, risk, group dynamics and technical aspects throughout the life cycle of the project. Special emphasis will be on the use of current P.M. software.

SCM 575 - Quality Management and Improvement (3) - The objective of this graduate course is to examine the primary quantitative and qualitative tools and methods used to monitor and control quality in organizations and evaluate the ways in which quality can be improved. Included in the course are such topics as the historical development of quality management, the seven basic tools for quality improvement, and management strategies for implementing world class quality improvement strategies. Particular emphasis is placed on the statistical tools used in control chart analysis and process capability study.