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14 February 2017
Find out about bachelor's degrees, postgraduate courses and all the educational courses offered by the UdG.
Teaching is concentrated in the faculties and schools, and the departments deal with research, which is also conducted by institutes and chairs, at the same time responsible for knowledge promotion.
The goal of this course is for students to understand the statistical foundations of data science, as well as specific techniques that are part of the body of methodologies used in data science.1. Techniques and concepts of statistical inference.2. Dimensionality reduction through projections.3. Generalised linear models.4. Non-linear modelling.5. Sampling techniques: cross validation, bootstrapping.6. Predictive model performance.
This subject aims to get students to understand and learn how to use the main techniques and algorithms in the two main aspects of automated learning 1.Supervised learning 1.1 Classification trees 1.2 Vector support machines 1.3 Neural Networks 1.4 Bayesian Methods 1.5 Ensemble models 1.6 Assessing/validating models 2.Unsupervised learning: 2.1 Clustering 2.2 Rules of association 2.3 Detecting anomalies 2.4 Self-organised Maps 2.5 Hidden Markov Models 2.6 Assessing/validating models
The goal of this course is to deepen students’ understanding of machine learning by studying different advanced data science techniques and applications 1. Deep learning 2. Transfer learning 3. Reinforcement learning 4. Convolutional networks for image processing 5. Text mining. Natural language processing 6. Recommendation systems
This course aims to introduce students to the technological tools for gathering data and how to prepare them so they can be used in data-science techniques.1.Main programming languages for data analysis 2.Data standards 3.Data sources and gathering 4.Data quality of data and cleansing
This subject aims to get students to discover the main methods for carrying out data-science projects as well as the complete implementation of projects with the aid of real-case studies.1.Methods for carrying out data-science projects 2.Legal and ethical aspects of data-science 3.Data life cycle, version monitoring 4.Feasibility and assessment of a data science project 5.Carrying out a data science project 6.Practical cases of data-science projects
This subject aims to introduce the techniques necessary for displaying data and explaining results from the application of data-science techniques.1.Displaying univariate and multivariate data.2.Visual presentation of results: storytelling, computer graphics, Edward Tufte principles.3.Dynamic and interactive displaying.4.Business analytics: reporting, Key Performance Indicators, dashboards.
This subject aims to get students to understand and learn data-science techniques through big-data-orientated tools and environments.1.Introduction to big data 2.NoSQL databases 3.Distributed file systems 4.Techniques and algorithms 5.Tools 6.Platforms 7.Machine Learning with NoSQL
The goal of this course is for students to learn about methods for specialised applications of data science, such as: optimisation and constraints for data science; web mining, graph properties, social networks; signal processing; omics data analysis, medical imaging, etc.
Educational action developed by a student in any collaborating institution, public or private, national or foreign, or in the university’s own units, with the aim of applying and adding to the learning acquired during their academic training, to bring the student closer to the reality in the professional field in which they will work and to develop skills that facilitate their incorporation into the job market.
The master's thesis will allow students to demonstrate the maturity and scientific-technical level they have reached during the education process.A report will be presented in writing and students will have to defend their work before a panel made up of the master's-degree teachers.
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