Checking date: 03/09/2019


Course: 2019/2020

Data Analysis
(15986)
Study: Bachelor in Computer Science and Engineering (218)


Coordinating teacher: SAEZ ACHAERANDIO, YAGO

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

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Programming Artificial Intelligence
Competences and skills that will be acquired and learning results. Further information on this link
General competences: - Analysis (PO a) - Abstraction (PO a) - Problem solving (PO c) - Capacity to apply theoretical concepts (PO c) Specific competences - Cognitive 1. Evaluation based on multiple Theoretical machine learning tasks (PO a) 2. Knowledge about several model building techniques working on data (PO a) 3. Knowledge about practical techniques to deal with uncertainty and errors in data to take advantage of them (PO c) - Procedimental/Instrumental 2. Students should use different Data Mining techniques, compare them through experiments, and analyze the results (PO b) 3. Students should apply the right and appropriate Data Mining technique and parameters to solve a task (objective) (PO c) - Attitudinal 4. Students should work on the homeworks in teams (PO d) 5. Students are required to use Data Mining tools and provide solutions to real-world problems through computer engineering (PO e) 6. Students must present a written summary for each homework, the final homework should be orally presented, and the final exam is written (PO g) 7. Students should be able to use state of the art Data Mining tools to solve homework tasks (PO k)
Description of contents: programme
1. Introduction to Data Analysis and Data Mining 2. Machine learning with numeric techniques 2.1. Statistical analysis and causal relations 2.2. Bayesian classifiers. Numeric and symbolic attributes 3. Numerical learning 3.1. Regression 3.2. Clustering with numeric techniques: K-means, Expectation Maximization 4. Evaluation of Machine Learning Models 4.1. Confusion matrices 4.2. Comparison of alternatives, significance contrasts 5. Attribute analysis 5.1. Non-supervised selection 5.2. Attribute transformation 5.3. Supervised selection 6. Methodology of data mining projects 6. Introduction to other advanced techniques (combination, SVM , Fuzzy systems, GAs)
Learning activities and methodology
Theoretical lectures: 2 ECTS. To achieve the specific cognitive competences of the course (PO a). Practical lectures: 2,5 ECTS. To develop the specific instrumental competences and most of the general competences, such as analysis, abstraction, problem solving and capacity to apply theoretical concepts. Besides, to develop the specific attitudinal competences. (PO a, c, d, f, g). Guided academic activities (present teacher): 1,5 ECTS. The student proposes a project according to the teachers guidance to go deeply into some aspect of the course, followed by public presentation (PO a, c, d, g, k).
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • I. Witten y E. Frank. Data Mining: Practical Machine Learning Tools and Techniques (Third Edition) . Morgan Kaufmann. 2011
  • Jesús García, Antonio Berlanga, José M. Molina, Miguel A. Patricio. Ciencia de datos: Técnicas analíticas y aprendizaje estadístico en un enfoque práctico. Altaria. 2018
Additional Bibliography
  • David Hand, Heikki Mannila. Principles of data mining. MIT Press. 2002
  • Jesus Garcia, José M. Molina. Apuntes de la asignatura. http://www.giaa.inf.uc3m.es/docencia/II/ADatos/apuntesAD.pdf. 2004
  • Pérez López, César. Estadística aplicada a través de Excel. Prentice Hall. 2002

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