Checking date: 10/07/2020

Course: 2020/2021

Decision support systems
Study: Master in Computer Engineering (228)


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

Type: Compulsory
ECTS Credits: 6.0 ECTS


Competences and skills that will be acquired and learning results.
Subject related competencies: CB6. Knowledge and understanding of opportunities in the development and/or application of ideas, often in a research context. CG4. Ability to mathematical model, calculate and simulate in technology centres and business engineering, especially in in research, development and innovation in all the areas related to Computer Science and related multidisciplinary fields. CG8. Ability to apply the knowledge obtained and solve problems in new or little-known environments in broader and multidisciplinary contexts, with the ability to integrate knowledge. CG11. Ability to communicate (orally and in writing) some conclusions - and the knowledge and reasons behind - to specialized and non-specialized audiences in a clear and unambiguous way. CE1. Ability to integrate general technologies, applications, services and systems of Computer Science into broader and multidisciplinary contexts. CE12. Ability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, intelligent and knowledge-based systems. Learning results: RA12 Critical awareness of the vanguard knowledge of his specialty. RA52. Comprehensive knowledge of applicable methods and techniques and their limitations RA23. Ability to use their knowledge and understanding in order to conceive models, systems and engineering processes. RA43 The ability to critically analyse the data and reach conclusions RA31. Ability to use their knowledge and understanding in order to provide solutions to be applied in problems using knowledge beyond those of the discipline. RA61. Demonstrate the generic competences of Bachelor´s graduates at the higher-level characteristic of the master's graduates.
Description of contents: programme
1. An Overview of Business Intelligence, Analytics, and Decision Support. 2. Foundations and Technologies for Decision Making. 3. Business Reporting, Visual Analytics, and Business Performance Management. 4. Data Mining. 5. Text Analytics, Text Mining, and Sentiment Analysis. 6. Web Analytics, Web Mining, and Social Analytics. 7. Automated Decision Systems and Expert Systems.
Learning activities and methodology
- Lectures (Theory): The aim is to teach the specific competences of the subject. The main knowledge that the students must acquire will be presented. In order to help to achieve this aim, the students will be given the corresponding class notes and reference texts that will help them to deal with the topic in which they are more interested in depth. The general aspects to create support decision computational systems will be a very relevant issue. - Seminars: In these sessions, the students will solve, with teacher's support, practical problems related to the use of decision support systems in the organizations. - Practice: The practice in this subject will be done in group and its aim is to design and create a decision support system. - Keynote speaker: An invited speaker will talk about some aspects of the subject. - Personal work: This activity is related to the development of different abilities such as auto organization and pacification of the individual work and the learning process.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Basic Bibliography
  • Daniel J. Power. Decision Support Systems. Concepts and Resources for Managers. Quorum Books. 2002
  • Efraim Turban, Jay E. Aronson, Ting-Peng Liang, Ramesh Sharda. Decision Support and Business Intelligence Systems (eighth edition). Pearson Prentice Hall. 2006
  • Efraim Turban, Ramesh Sharda, Dursun Delen. Decision Support and Business Intelligence Systems (Ninth Edition). Pearson. 2011
  • Efraim Turban, Ramesh Sharda, Dursun Delen. usiness Intelligence and Analytics: Systems for Decision Support. Pearson. 2014
  • Elizabeth Vitt, Michael Luckevich, Stacia Misnes. Business Intelligenge. Técnicas de análisis para la toma de decisiones estratégicas. McGraw Hill. 2003
Additional Bibliography
  • Kenneth N. Berk, Patrick Carey. Análisis de datos con Microsoft Excel. Thompson Learning.
  • Wayne L. Winston. Microsoft Excel. Data Analysis and Business Modeling. Microsoft.

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