S1. Fundamentals
of ResearchS2. Data Processing
and InfrastructureS3. Data Analysis
and ExploitationS4. Societal, Legal
and Ethic aspects
in Data Science
🟠 M1: Introduction to Research Methodology (1,5 ECTS). 🗓️ 🗓️
This course guides the students about techniques, most common standards and systems for the practice of scientific research and its methodological bases and documentaries. Topics covered include: General Approach (scientific knowledge and its purpose, problems of scientific research, research works); Scientific Work (choice of subject, setting objectives, formulating hypotheses, choice of work method, choice of tools and resources. Phases of work); Information Search (sources, publications, bibliographical searches, access to scientific documentation, internet, etc.); Work Writing (rules, principles, tips, style, language, etc.) and Presentation and Defense of Work (legal aspects, formal aspects, personal aspects, visual aids to support the presentation).
🟠 M2: Data Visualization (3 ECTS). 1st 🗓️
This course will allow the student to gain the fundamentals for the visualization of all kinds of information. With an eminently practical approach, the technologies and fundamentals necessary to create successful information visualization tools will be presented.
🟠 M3: Big Data (3 ECTS). 1st 🗓️
This course will allow the student to gain the fundamentals for the analysis of large volumes of data. With an eminently practical approach, the technologies and fundamentals necessary to successfully accomplish the whole data analysis process will be presented in the context of Big Data, from the raw data to the models derived from them.
🟠 M4: Cloud Computing and Big Data Ecosystems (4,5 ECTS). 1st 🗓️
This course presents traditional data management systems and architectures for scalable distributed systems and data management systems: bigtable, data streaming, persistent queues.
🟠 M5: Data Processes (4,5 ECTS). 1st 🗓️
The goal of this course is to go through the complete data science process. We will learn: i) how to understand the goals of a project and translate it to knowledge discovery goals; ii) to explore data to understand it, iii) to prepare data sets iv) to model, validate and evaluate models, v) communicate results and vi) deploy. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving real life problems.
⚪ O1: Programming for Data Processing (4,5 ECTS). 2nd 🗓️
This course is related with data manipulation and programming using the Python language. The main goal is to introduce main characteristics and programming style using this language of wide adoption for data analysis purposes. Once introduced the language, the course presents to students how to efficiently use the different constructs, control statements and data structures in Python. Based on this, the course describes the main characteristics of a Python framework for data storage and manipulation. All programming concepts presented in the course are accompanied with exercises to guarantee correct comprehension and practical knowledge.
⚪ O2: Data Science Seminars (4,5 ECTS). 2nd 🗓️
The course is configured around a series of seminars that address the challenges and opportunities emerging from large quantities of heterogeneous, complex, networked and dynamic data influencing virtually all socio-economic domains.
🟠 M6: Statistical Data Analysis (4,5 ECTS). 1st 🗓️
This course is intended to be a non-exhaustive survey of techniques to turn multivariate data into useful information so that informed decisions can be made. The perspective is twofold, theoretical and applied, covering topics such as: exploratory data analysis, multivariate statistical summaries and graphical representations, dimensionality reduction, regression techniques and time series analysis. There will be an emphasis on hands-on application of the theory and methods throughout, with extensive use of R.
🟠 M7: Machine Learning (4,5 ECTS). 1st 🗓️
Machine Learning transforms data into knowledge and provides general-purpose systems able to make rational decisions. This course presents several methods to solve classification problems, both supervised and unsupervised, methods to find probabilistic relationships between system variables and models of spatial statistics.
🟠 M8: Deep Learning (3 ECTS). 1st 🗓️
Deep learning has emerged from the connectionist branch of machine learning, aided by the arrival of big data and increased computational power (e. g., parallelization using graphics processing units – GPUs). Deep learning has shown better performance than other approaches to solve problems that cope with large amounts of data as it is required, for example, in computer vision (image or video processing) or speech understanding. This course presents a theoretical and practical view of deep learning. It begins with the introduction of the foundations of artificial neural networks and different types of architectures (both shallow and deep networks). Then, the course presents learning techniques to train neural networks, with special attention to deep learning methods. The course also presents neural models for problem classes and application domains (e.g., computer vision and natural language processing). To complement the practical view, the student will use specialized software tools to train neural networks in practical problems.
🟠 M9: Open Data and Knowledge Graphs (4,5 ECTS). 1st 🗓️
Deep learning has emerged from the connectionist branch of machine learning, aided by the arrival of big data and increased computational power (e. g., parallelization using graphics processing units – GPUs). Deep learning has shown better performance than other approaches to solve problems that cope with large amounts of data as it is required, for example, in computer vision (image or video processing) or speech understanding. This course presents a theoretical and practical view of deep learning. It begins with the introduction of the foundations of artificial neural networks and different types of architectures (both shallow and deep networks). Then, the course presents learning techniques to train neural networks, with special attention to deep learning methods. The course also presents neural models for problem classes and application domains (e.g., computer vision and natural language processing). To complement the practical view, the student will use specialized software tools to train neural networks in practical problems.
⚪ O4: Time series Data Mining (3 ECTS). 2nd 🗓️
In this subject the student will explore areas of Knowledge Discovery less known, but increasingly relevant. There are domains where information is presented mostly in the form of Time Series which require a very specialized treatment. Examples of these are medical domains such as Electrocardiography or Audiometry, financial domains, etc. Time series are a challenge to the traditional techniques of Data Mining and often require the use of novel solutions. We will discuss traditional numeric Time Series Techniques, novel approaches and will pay special attention to Symbolic approaches.
⚪ O5: Graph Analysis and Social Networks (3 ECTS). 2nd 🗓️
Social computing is a general term for an area of computer science that is concerned with the intersection of social behavior and computational systems. During recent years the Internet introduced a social element where users could network, share interests, publish personal insights and use their computers for more than just doing a job faster, and this has led to the development of social machines where both humans and machines collaborate to solve social problems. This course presents the principals of social computing and focuses on graph and network analysis.
⚪ O6: Image Processing, Analysis and Classification (4,5 ECTS). 2nd 🗓️
This subject covers techniques for image processing and analysis techniques, as well as methods for image classification. Morphological approaches will be covered within the image processing and analysis. For image classification, relevant features for clustering and learning will be treated. Approaches and applications for image indexation and image searching will be studied.
⚪ O7: Information Retrieval, Extraction and Integration (4,5 ECTS). 2nd 🗓️
The amount of available data in most scientific areas has grown dramatically during the last few years. However, this increment did not have a parallel impact on the knowledge available for decision-making. There is a need for automated models to manage such data, considering that human beings will never directly use most of it. The course Information Retrieval, Extraction, and Integration focuses on the necessary methods and tools to extract information and models to efficiently retrieve data for further integration. These are critical tasks to provide relevant information for decision making, whose complexity increases with the amount of data available. As application areas, we focus mainly on biomedicine due to the complexity and the specific requirements.
⚪ O8: Intelligent Systems (4,5 ECTS). 1st 🗓️
The amount of available data in most scientific areas has grown dramatically during the last few years. However, this increment did not have a parallel impact on the knowledge available for decision-making. There is a need for automated models to manage such data, considering that human beings will never directly use most of it. The course Information Retrieval, Extraction, and Integration focuses on the necessary methods and tools to extract information and models to efficiently retrieve data for further integration. These are critical tasks to provide relevant information for decision making, whose complexity increases with the amount of data available. As application areas, we focus mainly on biomedicine due to the complexity and the specific requirements.
🟠 M10: Societal, Legal and Ethic aspects in Data Science (3 ECTS). 2nd 🗓️
Virtually every data scientist and AI professional will have to cope with legal and ethical issues during the professional career. Designing, developing or using AI systems or data intensive applications imply knowing and abiding the law (legal compliance) and being aware of the technology impact and acting accordingly (ethical responsibility). During this course, the student will learn the European Union policies and regulations around AI and data. In particular, students will learn the basics of the forthcoming AI Regulation and the AI Liability Directive, the General Data Protection Regulation, the Data Governance Act, the Data Act, some copyright law including the Database Directive. Open Data policies in Europe will also be taught.
The first semester starts in early September with the course Introduction to Research Methodology taught intensively for three afternoons. This course is offered again in January-February. The student only must take it once.
On the first semester, 7 mandatory courses and 1 elective course (“Intelligent Systems”) are offered. The second semester runs from February to April. Two mandatory courses and six elective ones are offered. The rest of the semester, until July, is devoted to the development of the Master’s Final Project.
Agree on the topic
Students can contact their teachers to define the topic of their dissertation. Early communication with faculty is crucial to select a topic of mutual interest. This collaboration ensures the relevance of the topic and allows students to receive guidance and develop research skills with expert guidance.
Selection of proposals
Alternatively, during the month of December, students will receive a file with various proposals for dissertations by MUCD professors and will send to the degree coordinator their preferences about the possible proposals for Master’s Thesis, identifying in order up to a maximum of 5 proposals that most appeal to them.
Make proposal
In the event that they are not attracted by any offer, or have not been assigned any of the selected ones (several students can select the same proposal), the student must make one and send it to the coordinator, placing it in one of the MUCD subjects and indicating up to three teachers from the same subject who can act as directors.
Master’s Final Project Report
The student must submit the dissertation report as a PDF within seven days before the defence.
UPM Digital Archive
If you are interested in publishing your work in the UPM digital archive, complete these authorization forms: (instance 1/2, instance 2/2)
Confidentiality request
If you don’t want your work in the UPM digital archive, submit an (instance) for TFM confidentiality of your TFM.
*Do not fill out ‘Speciality’, ‘Doctor’, and ‘Mention”
What is the Master’s structure?
How is the supervisor for the Master’s Final Project (MFP) appointed?
- Students may contact the master’s professors and agree on the topic of their MFP.
- In December, a list of proposals for MFPs designed by the teachers of the master and other staff at the School of Computer Engineering will be published. Students would express their preferences on the most appealing ones through an online form launched by the Master’s Coordination.
- In the case that a student does not have ideas regarding their MFP, or in the case they are not assigned to any of their preferred proposals (several students can select the same proposal), the student must make a proposal, classifying it in one of the courses offered in the master and suggesting up to three professors that can exert as directors.