Data analysis and Big data

The objective of the course is for students to understand and use various methods for decision-making in the business context. These methods include data analytics, data mining, search and optimization, and artificial intelligence and expert systems.

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. Intelligent 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

The design of the course is adapted to the blended nature of the master's degree: in person + remote Learning activities are summarized as follows: AF1: Lectures: Theoretical presentations accompanied by digital presentations AF3: Theoretical and practical classes: Combination of lectures accompanied by the resolution of practical exercises AF5: Tutorials: Personalized on-site or remote tutorials AF2: E-learning activities: Remote activities that the student develops independently. These activities include: Participation in forums, viewing pre-recorded contents, and guided exercises AF7: Individual work of students: Individual student activities that complement the other activities (both classroom and non-classroom) and exam preparation AF6: Work in groups Teaching methodology MD1: Teachers give lectures with support of digital presentations, in which they develop the subject. MD3: Practical cases that are solved with a guided provided by the teacher. MD5: Individual or group preparation of practices and reports MD6: Specific e-learning activities including visualization pre-recorded content, self-review activities, participation in forums, etc.