Checking date: 10/05/2024

Course: 2024/2025

Master's Thesis
Master in Big Data Analytics (Plan: 352 - Estudio: 322)

Coordinating teacher: RUIZ MORA, CARLOS

Department assigned to the subject: Computer Science and Engineering Department, Signal and Communications Theory Department, Statistics Department, Telematic Engineering Department

Type: Master Final Project
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
All courses in the Master.
Basic competences The students should be able to apply the knowledge obtained through the Master and their problem solving abilities to new situations within multidisciplinar settings related to their fields of interest The students should be able to use their acquired knowledge to make judgements based on incomplete or limited information. These judgements should take into account aspects related to social and ethical responsibility issues The students should be able to communicate their conclusions and the knowledge that supports them to both specialized and general audiences in a clear and unambiguous manner The students should possess the learning skills that would allow them to continue their studies in an autonomous and self directed manner General competences To identify the data analysis techniques that are more suitable for a given problem. To know how to apply them to the analysis, design and solution of these problems To obtain practical and efficient solutions to treat large data sets, both individually and as part of a team To apply data analysis techniques to real large-scale data, including Web data To summarize the conclusions obtained from this analysis and to present it in a clear and convincing manner to a bilingual audience, both orally and in writing To be able to generate new ideas (creativity) and to anticipate new situations, in the context of data analysis and decision making problems Specific competences To identify the opportunities that the data analysis techniques may offer for the improvement of the activities of organizations and companies To use advanced statistical techniques in the treatment of large data sets in areas such as estimation, inference, forecasting or classification, and to apply them in an efficient manner To design systems for data processing, from their collection and initial processing, to their statistical treatment and the presentation of the final results To identify opportunities for the application of machine learning techniques to the solution of real problems To conduct the analysis and design of computer applications based on machine learning techniques To apply advanced data treatment procedures to problems in areas of special relevance to society To use advanced techniques in the treatment of large data sets To make use of distributed platforms for the distribution of content and of techniques for the maintenance of their topology To make decisions for e-learning systems to improve learning processes based on the information extracted from learning applications and systems To understand and use in an efficient manner the architecture of data centers, including their computation systems and their communications Learning results - To be able to apply the techniques presented in the different subjects of the Master program to the analysis of data from a specific problem - To obtain results that can be applied to the improvement of the activities of an organization or company/To be able study in depth advanced data analysis procedures - To be able to present results and conclusions in a clear and effective manner - To make use of all the knowledge and competences acquired throughout the Master program
Skills and learning outcomes
Description of contents: programme
The Master's Thesis is organized as an exercise in the treatment of data and its analysis to improve the performance of a relevant organization or company. The students will be offered different alternative fields in which to complete this Thesis. They will also receive support and orientation throughout the completion of the Thesis. The students will collect the data of interest, apply the relevant techniques to these data, and present the results in a clear and useful manner. As an alternative, it will also be acceptable to conduct a study in depth of some advanced data analysis technique for large-scale data sets. This study would include both theoretical and computational aspects relevant to its efficient implementation.
Learning activities and methodology
Learning activities Tutorial clases Individual work of the student Methodology Reading and critical commentary of texts recommended by the class instructor: news articles, reports, textbooks and/or scientific journal articles. These readings should be discussed in class, or at least they should provide a basis to expand and consolidate the knowledge required to complete the Master Thesis. Preparation of individual or team reports or homeworks. TFM specific regulations:
Assessment System
  • % end-of-term-examination 100
  • % of continuous assessment (assigments, laboratory, practicals...) 0

Calendar of Continuous assessment

Assessment Matrix
Detailed subject contents or complementary information about assessment system of B.T.
Additional information

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