In addition to the 27 hours of General Course Requirements, the Business Analytics Concentration requires the following 9 hours:
- CIS 5860: Applied Analytics Project (1-6): Students will work on a real world project taking raw data through the preparation, management and analysis phases to the presentation of results and recommendations from the analysis.
AND Choose 6 hours from the list below:
CIS 5630. Data Management (3): Data mining is a broad area dealing with the analysis of a large volume of data that integrates techniques from several fields including machine learning, statistics, pattern recognition, artificial intelligence, and database systems. Data mining is a rapidly growing field that supports decision-making by detecting patterns, devising rules, identifying new decision alternatives and making predictions about the future. The course objective is to present the leading data mining methods and their application to real-world problems. The course is organized around a number of well-defined data mining tasks such as description, classifications, estimation, predictions, and affinity grouping and clustering. The topics covered include: introduction to knowledge discovery in the databases (KDD), statistical methods, emerging modeling techniques such as neural networks, and others.
CIS 5830. Security Privacy and Ethical Issues in Business Analytics (3): This course is designed as a broad overview of important security, privacy, ethical and societal issues that are relevant to the field of Business Analytics.
MBA 5870: Analytical Models for Supply Chain Management (3): Analytical models for supply chain management focuses on the applications and development of modeling tools for the supply chain. This course introduces students to important supply chain problems and solution methodologies including optimization, simulation, and other analytical methods. The objective of the course is to develop valuable modeling skills that students can appreciate and use effectively in their careers.
ECO 5740: Forecasting and Time Series Models (3): An examination of time series models for purposes of forecasting and performing time series regressions in economics, business, and the social sciences. Topics covered may include ARIMA, VAR, Granger causality, unit roots, spurious regressions, ARCH, and GARCH. Computer software will be utilized in applications.
CS 5710: Data Mining and Knowledge Discovery in Scientific Data (3): Large quantities of data are collected in different studies and/or experiments in science, engineering, business, and medicine. The data contain significant amounts of useful information or knowledge that is often hard to discover without computational tools and techniques. This course focuses on techniques used in data mining tasks such as classification, association rule mining, clustering, and numerical prediction. The approach utilizes visualization, statistics, and neural networks. The goal is to study data mining as a means to achieve knowledge discovery in databases.