This course is designed to expose participants to the development and utilization of forecasts. Emphasis on statistical methods (time series, regression, Monte Carlo simulation etc.) and software applied to quantitative forecasting. Specific applications include forecasting sales, market prices, inventory, and macroeconomic factors.

This course is designed to equip organizational leaders to make use of qualitative and quantitative data analysis for effective decision making within various types of organizations. Students explore and practice the use of quantitative techniques, such as simple and higher level statistics, probability, investment analysis, and financial statement trend analysis. These techniques and the presentation of results are applied in real-world business situations through business cases.

The data mining course presents methods and systems for mining varied data and discovering knowledge from data. After detailing a data mining system architecture and tasks, the course examines and compares specific methods in data mining, such as data preparation, classification, clustering, and text mining. Several applications are detailed, and tools to build new applications are provided. The task of knowledge discovery is then outlined as a higher-level goal of data mining. Familiarity with statistics, and database systems, in particular database design, is expected.

This course provides an introduction to frequently used data analysis techniques that can aid business decision making. Data capture, processing, analysis, and presentation techniques will be introduced. Excel and other relevant software tools will be used to perform basic data analysis.

A course designed to explore statistical methods for data analysis using contemporary data analysis tools. Topics include: planning statistical procedures for data analysis, statistical research design principles, planning data files for analysis, using computer software for data analysis, extended study of analysis of variance (ANOVA), completely randomized designs, factorial designs, randomized block designs, split-plot designs, crossover and repeated measure designs, and response surface designs.