Checking date: 21/02/2025 13:25:09


Course: 2025/2026

Data Analysis
(19479)
Bachelor in Computer Science and Engineering (Plan: 570 - Estudio: 218)


Coordinating teacher: PATRICIO GUISADO, MIGUEL ANGEL

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Learning Outcomes
K12: Knowledge and application of the fundamental principles and basic techniques of intelligent systems and their practical application. KOPT_1: To know and understand in depth advanced technologies in a specific area related to computer engineering, which constitute the state of the art in their area of study, including emerging trends and recent developments. KOPT_2: To interpret scientific and technical information sources to deepen knowledge in a specific area related to computer engineering. S5: Ability to know and develop computational learning techniques and to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data. SOPT_1: To identify, assess their technical feasibility, and apply advanced tools, methodologies, and technological solutions used in the degree field, in order to develop algorithms or systems that integrate cutting_edge and innovative technologies. SOPT_2: To apply analytical and design methodologies to solve advanced problems in the field of computer engineering, and evaluate the performance and limitations of different technological approaches, proposing improvements and alternatives. COPT_1: To conceive and develop projects that integrate advanced knowledge and provide innovative solutions in the field of study of computer engineering.
Description of contents: programme
1. Introduction to Data Analysis and Data Mining 2. Machine learning with numeric techniques 3. Numerical learning 4. Evaluation of Machine Learning Models 5. Attribute analysis 6. Methodology of data mining projects 7. Introduction to other advanced techniques (combination, SVM , Fuzzy systems, GAs)
Learning activities and methodology
Seminars and lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work with the option of oral or written presentation. Individual and group tutorials to resolve doubts and queries about the subject. Internships and directed laboratory activities.
Assessment System

The course syllabus may change due academic events or other reasons.