The aim of the course on Data Analysis is to provide the philosophy and the main methods for extracting the information contained in the data. It covers the preparation of the data, the exploratory analysis, the visualization of the information, the modeling of patterns and its implementation in computer systems.
Person in charge
Jan Graffelman (
Jose Antonio Sánchez Espigares (
CE1 - Skillfully use mathematical concepts and methods that underlie the problems of science and data engineering.
CE2 - To be able to program solutions to engineering problems: Design efficient algorithmic solutions to a given computational problem, implement them in the form of a robust, structured and maintainable program, and check the validity of the solution.
CE3 - Analyze complex phenomena through probability and statistics, and propose models of these types in specific situations. Formulate and solve mathematical optimization problems.
CE4 - Use current computer systems, including high performance systems, for the process of large volumes of data from the knowledge of its structure, operation and particularities.
CE8 - Ability to choose and employ techniques of statistical modeling and data analysis, evaluating the quality of the models, validating and interpreting them.
CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
CT4 - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
CT6 - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
CT7 [Avaluable] - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.
CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
Generic Technical Competences
CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.
CG3 - Work in multidisciplinary teams and projects related to the processing and exploitation of complex data, interacting fluently with engineers and professionals from other disciplines.
CG4 - Identify opportunities for innovative data-driven applications in evolving technological environments.
Student do an exploratory analysis of a data set and hand in a questionnaire about it. Objectives:1234 Week:
8 (Outside class hours) Type:
Students realize, in couples, a complete multivariate study of a certain dataset using the techniques they studied during the course, and hand in a written report about it. Objectives:1234 Week:
15 (Outside class hours) Type:
Exam concering basic concepts
There are two exams related to the theoretical concepts of the course. Objectives:1234 Week:
The learning process is a combination of theoretical explanation and practical application. The theory classes are used to explain the basic scientific contents of the course, whereas the laboratory sessions work on their application to solve real-life problems.
Practicals and project form the basis for working out the transversal competences of the students, related to team-work and public presentation of results. Practicals and project also serve to integrate the different pieces of knowledge of the course.
For hands-on computer training we use the R statistical environment.
The student's final grade for the course is based on grades obtained for weekly homework assignments (25%), a partial exam half-way the course (25%), a final exam covering the second half of the course (25%) and a project (25%).
Each weekly assignments consists of resolving a questionnaire. These assigments aim at consolidating knowledge of the techniques exposed in the theoretical sessions. The assignments require analysis of datasets in the statistical environment R.
A project is carried out by a group of two students, and students have to show they can resolve problems with the techniques they have learned during the course. Each group hands in a written report about their project at the end of the course.
The two exams will be programmed according to the calendar of the faculty, and evaluate if students have assimilated the basic concepts of the material of the course.
For the resit exam, the student can choose to do a re-examination of only the first partial (25%), or of only the second partial (25%), or of both partials (50%). The re-evaluation thus represents at most 50% of the final course grade.