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]
