Basic skills in quantitative modeling. Managerial decision models to complex business problems. The uses of analytical techniques including optimization, Monte Carlo simulation, and decision trees via Excel commands, tools and add-ins. Analyses of business problems in operations, finance and marketing as illustrative examples and their economic interpretation.
Introduction to various techniques to extract useful information from the large volumes of company data. Data mining as a concept . Relation of data mining techniques to specific business analytics situations. Classification and prediction. Association rules and clustering.
The role of research in solving managerial problems and making better decisions; various functions under business intelligence and their interrelated nature. Analysis and transformation of data for use in strategic decision making. Development of managerial skills in planning, conducting, communicating, reporting and evaluating research projects and technical skills in analyzing data. Use of research results to understand the constantly changing business world.
Overview of the modules of demand management systems: Demand forecasting system, customer order entry systems, order confirmation systems, shipment systems, and production planning systems. Pyramid forecasting in the supply chain. Overview of quantitative, subjective and hybrid demand forecasting methods. Cost of demand forecast error measures in merchandising and procurement.
Differences between forecasts, causal explanations, targets and plans. Forecasting in production, finance, accounting and marketing. New product demand forecasting. Low-volume demand forecasting. Time-series forecasting for high-volume products. Demand forecasting for discontinued products. Overview of forecasting software. Experimental design for forecasting method selection. Combinations of forecasts.
Analysis of real-world situations on which large-scale data analysis may create significant competitive advantage. Use of data and potential pitfalls. The use of software such as R and SQL to create knowledge for decision making. Fundamentals of statistical modeling, machine learning, and data-driven decision making. Application of these topics on current business problems with mathematical notation, algebra, calculus, probability, and basic statistics.
Time-series forecasting. Empirical evidence. Forecasting competitions. A quantitative overview of time-series forecasting methods. Components of time-series data. Decomposition of time-series. Random walk models. (Weighted) moving average models. Time-series regression and GARCH models. Exponential smoothing models. ARIMA and SARIMA models. Intervention analysis, identification of pulses and outliers. Multivariate methods: OLS, GLS, ERLS, Logit, Probit, MARIMA and State Space.
The conventional economic paradigm of rational agents in an efficient market. The use of psychology as a guide to explain the abundant empirical evidence violating the conventional wisdom. Behavioral biases and alternative preferences explaining investor behavior. Irrational trading decisions and distortions in the financial markets. market inefficiencies. Behavioral analysis in corporate decision making. Evidence for both views in the context of capital structure, investment, dividend, and merger decisions.
Contemporary topics and issues in data management.
