19 courses with the subject BAN, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BAN 280Statistical Analysis for Business and Economics3
Classification and presentation of business and economics data, probability and expected value, statistical inference, simple linear regression and correlation analysis.
Seminar in selected topics in Business Analytics. This course does not fulfil requirements towards any major or concentration in the Cameron School of Business.
An analytics professional/practitioner is expected to be well-versed in modern analytics tools and techniques. In this course, students will gain competence in and exposure to relevant analytics software tools. The course presents software tools in the context of real-world datasets and problems.
Application of statistical methods to business problems; topics include chi-square analysis, the F distribution, one-way and two-way analysis of variance, simple and multiple regression analysis, and introduction to forecasting with regression models; use of computer programs in solving statistical problems.
This course provides an introduction as well as hands-on experience in descriptive analytics and data visualization. It introduces students to principles for creating meaningful analysis and displays of quantitative and qualitative data to facilitate managerial decision-making.
This course develops an understanding of analytics in the healthcare field. Emphasis is given to how technology and analytics impact healthcare. Analytical tools are used to solve real healthcare challenges, improve processes, and help make important healthcare business decisions.
This course introduces fundamental analytics concepts in the context of sports. A variety of sports, professional and amateur, are covered with an emphasis on using analytics techniques and tools to add value and fuel competitive advantage. Software tools will be used extensively to illustrate concepts.
This course introduces AI technologies for data-driven decision-making in business environments. Topics include data processing, regressions, neural networks (NN), and Recurrent Neural Networks (RNN), with an emphasis on their real-world applications. Students will develop critical thinking and analytical skills by exploring how AI can enhance business insights and optimize operations.
Acquisition, management, and analysis of large sets of data that are often encountered in a business environment. Focuses on challenges specific to analysis of data at scale. Real-world applications and case studies are examined.
Leveraging data to add value to business is of critical importance in our modern society. This course will focus on the analysis of data in order to create predictions and will examine a variety of predictive techniques to extract information from data. These topics will include: multiple linear regression, logistic regression, classification, clustering, text mining, neural networks, and others. This course will also discuss the challenges presented by large datasets and describe techniques such as data cleaning and visualization.
Prescriptive Analytics deals with developing analytical models to solve business problems. Prescriptive models are used when a business needs to optimize operational decisions. The solution from these models provide the best solution (profit maximization, cost minimization, minimization of resource utilization, etc.) to the operational decision problems. Students will use MS Excel and AMPL as software tools for optimization in this course.
This course develops skills for solving complex business problems with the aid of data analytical techniques in the transportation field. Students will develop business analysis frameworks and apply Excel-based optimization tools to assess transportation solutions.
SCM 370 and admission to the Cameron School of Business. Course Repeatability: Course may not be repeated.
BAN 417Supply Chain Analytics3
This course introduces students to quantitative models the firms use to manage supply and demand. Managing supply and demand involves managing customer preferences, classify suppliers and customers, optimize product pricing, and promotion strategies. The applications of the above models in the hospitality industry, broadcasting, show business are discussed.
Development of an approach to forecasting through the study of time-series techniques including moving averages, exponential smoothing, regression, and Box-Jenkins.
Introduction to simulation models as tools in the analysis of business problems. Discrete event simulation models for production and service systems are introduced through the use of a computer simulation language.
Involves the application of business analytic skills developed in the academic environment to problems in a real-world operating environment. The participant will receive practical training and experience under the guidance of the staff of a local business or government organization and a faculty supervisor. Available internships are filled on a competitive basis.
