Business analytics & decision support

URL study guide

Description

Last year course is taught: 2024
Last examination year: 2025
2025-2026 replaced by 1BV70

The course covers data analytics methods, fuzzy decision-making and, interpretability to  support data-driven business decisions.  . Analytics methods are meant for data-intensive decision problems. Predictive modeling, different types of modeling problems such as classification, regression, model evaluation and model deployment are discussed. Fuzzy decision making is discussed for cases where there is uncertainty in the data. Fuzzy inference systems and fuzzy cognitive maps are amongst the methods discussed. Intrinsically interpretable models (white-boxes) and various model agnostic methods are introduced for obtaining explanations of the decision models. Students also obtain hands-on experience in using the relevant tools through the assignments.

Objectives

• distinguish the goal of a business and select the convenient canonical data mining task for the corresponding research question.
• distinguish the appropriate data mining models to solve a given research question.
• discuss data analytics models for the supervised or unsupervised learning and their corresponding performance measures.
• conduct model validation and evaluation procedures to develop a data analytics model for solving a research question.
• perform data analysis by using several benchmark models and comment on their performances in a comparative way.
• model decision making problems with fuzzy logic and fuzzy set theory
• design Fuzzy Cognitive Map-based models for complex systems, perform what-if simulations and analyse the results in a decision-making context
• discuss the interpretability and explainability methods available for different prediction models

Method of Assessment