- Big Data and Business Analytics (Course 3 - First Semester)

The aim of the course is to provide the student with the necessary knowledge about the applications of Artificial Intelligence, through its various paradigms, in the solution of business-related problems. In the same way, the student will learn the principles, methods and techniques of Artificial Intelligence and its application in business objectives.

1. Introduction a. Context. b. Key Features c. Main techniques 2. Data mining a. Introduction b. Applications c. KDD and Data Mining d. Data Mining Tasks e. Applications f. Case studies 3. Artificial Neuron Networks a. Introduction b. Artificial Neuron Networks c. Applications of ANNs d. Advantages and Disadvantages e. Case studies 4.Text mining a. Definition b. General architecture c. Basic Operations d. Applications e. Case studies 5. Web Mining a. Introduction b. Web Content Mining c. Structure Web Mining d. Usage Web Mining e. Case studies 6. Knowledge-Based Systems a. Fuzzy Logic and the Fuzzy Concept b. Fuzzy Logic c. Fuzzy reasoning systems d. Applications e. Case studies 7. Programming languages for AI a. Introduction b. Most commonly used languages c. How these languages work. 8. Ethics and AI a. Introduction b. Risks associated with AI. c. Questionable application cases. d. Initiatives for ethical AI.

- Lectures. The aim is to achieve the specific competencies of the subject. The fundamental ideas of the subject will be presented. - Practical classes. These classes develop the specific instrumental competencies and most of the transversal competencies, such as teamwork, the ability to apply knowledge to practice, to plan and organize and to analyze and synthesize. They also aim to develop specific attitudinal skills. - Academic Activities: -- With the presence of the teacher. Participation in classes guided by the teacher where aspects of the subject are studied in-depth and evaluated. Tests to evaluate theoretical and practical knowledge. -- Without the presence of the teacher. Exercises, complementary readings proposed by the lecturer, class preparation, practical work. - Webinars. These are intended to complement the acquisition of specific cognitive competencies. In addition, they develop some transversal competencies such as the capacity for analysis and synthesis, as well as teamwork. - Tutorials: Individualized assistance (individual tutorials) or group assistance (group tutorials) to students by the lecturer.