Checking date: 08/06/2021

Course: 2021/2022

Decision Support Systems
Study: Master in Computer Technologies Applied to the Financial Sector (313)

Coordinating teacher: TOLEDO HERAS, MARIA PAULA DE

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Data analysis and Big data
Subject related competencies: - Ability to understand the need for computerized support of managerial decision making. - Ability to create and design decision support systems (DSS). - Ability to apply and integrate the acquired knowledge in order to solve problems in new and multidisciplinary environments. Learning results: - Ability to understand the need of business computerized decision support. - Be capable of designing a decision support system in a business environment. - Be capable of evaluating and choosing from different decision support systems. - Be familiar with the mathematical, statistical and artificial intelligence methods used in the design and construction of decision support systems. - Be capable of designing and creating intelligent systems able to make decisions in the resolution of problems with Bussiness Intelligence
Skills and learning outcomes
Description of contents: programme
Unit 1. Introduction to Decision support systems and business intelligence - Decision support systems - Phases of the Decision-Making Process - Models in decision making - Computerized systems for decision making - Business Intelligence - Analytics: Descriptive Analytics, Predictive Analytics, Prescriptive Analytics Unit 2. Descriptive analytics and visual analytics - Data preparation: Data warehousing; ETL process: extract, transform and load - Data description: OLAP Online Analytical Processing ; visual analytics; Business reporting; - KPI and Dashboards - Business Performance Management: (Balanced scorecards); BPM Technologies and Applications - Performance Dashboards and Scorecards Unit 3. Predictive analytics and data mining - Introduction: predictive analytics; data mining; knowledge acquisition; methodologies (Crisp-DM, Knowledge Discovery in databases) - Modeling and evaluation - Association rule mining - Text mining and sentiment analytics - Web analytics, web y mining social analytics Unit 4. Decision support using models - Prescriptive analytics in DSS - Model-Based decision making - Certainty, Uncertainty, and Risk - Mathematical models for Decision support - Lineal Programming (Optimization) - Uncertainty: Sensitivity Analysis, What-If Analysis, and Goal Seeking - Support Systems Modeling with Spreadsheets - Decision Analysis - Problem-Solving Search Methods - Simulation Unit 5. Expert systems - Artificial Intelligence - Expert systems - Structure of Expert Systems - Knowledge Engineering - Rule based expert systems - Inference with uncertainty - Expert systems in the financial sector - Development of Expert Systems Unit 6. Knowledge management systems and collaborative systems Tema 7. DSS in the financial sector
Learning activities and methodology
Master clasess Practices and labs Tutorships e-learning activities Individual work Group project (Case study)
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • Ramesh Sharda, Dursun Delen, Efraim Turban. Business Intelligence and Analyitics. Systems for Decision Support. Pearson. 2014
  • Ramesh Sharda, Dursun Delen, Efraim Turban. Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th edition. Pearson. 2019
Detailed subject contents or complementary information about assessment system of B.T.

The course syllabus and the academic weekly planning may change due academic events or other reasons.