Business analytics, applied modelling and prediction

The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.

  • Introduction to data analysis and decision-making.
  • Time series data.
  • Outliers and missing values.
  • Pivot tables.
  • Probability distributions.
  • Decision making under uncertainty.
  • Methods for selecting random samples.
  • Nonparametric tests.
  • Stepwise regression.
  • Time series forecasting.
  • Regression-based trend models.
  • The random walk model.
  • Autoregressive and moving average models.
  • Exponential smoothing.
  • Seasonal models.
  • Introduction to linear programming.
  • Product mix models.
  • Sensitivity analysis.
  • Monte Carlo simulation.
  • Applied simulation examples.

If you complete the course successfully, you should be able to:

  • apply modelling at varying levels to aid decision-making
  • understand basic principles of how to analyse complex multivariate datasets with the aim of extracting the important message contained within the large amount of data which is often available
  • demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations and possible misuse.
  • Albright, S., W. Winston and C.J. Zappe. Data Analysis and Decision Making, South-Western, fourth edition (2010) [ISBN 9780538476126]

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