600
For graduate students only. This course is a survey of quantitative techniques used in business decision-making, including linear programming, forecasting, decision analysis and queuing analysis. Students will develop spreadsheet models that enable these techniques to utilize the data available to them and apply the results to business decisions.
Credit Hours: 4
Prerequisites
Foundation courses.
Most business planning begins with a forecast or a prediction. We cover the most useful predictive analytics models, whether you are a financial analyst, an operations manager, an accountant, a marketer, a human resources manager or an entrepreneur. We use actual data much like the data you encounter in practice. Models are explained as procedures that you may replicate with your own data. These include moving-average, exponential smoothing, S-curve, event, advanced regression, time-series decomposition, ARIMA, data mining, ensemble, and text mining models. We employ Excel-based ForecastX and Analytic Solver, two widely-used forecasting and data/text mining software in practice.
Credit Hours: 4
Prerequisites
Foundation courses.