Checking date: 13/05/2022

Course: 2022/2023

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 may change due academic events or other reasons.