Course Content

This semester focuses on fundamental concepts to become a proficient Big Data practitioner. Accordingly, it aims at training students in understanding how data management can scale to large volumes while potentially also dealing with velocity and variety; fundamental concepts to conduct rigorous data analysis with Machine Learning techniques and basic knowledge to assess the viability of a business idea. Additionally, the students will mandatorily take an ethics course and will learn how to autonomously study a new emerging field on their own. This semester has a strong practical component and students, divided in teams, will define and implement their own Big Data project, transversal to all courses, potentially ready to be continued as a start-up. The semester consists of the following courses:

• Big Data Management (BDM, 6 ECTS, Prof. Alberto Abelló). In this course, students will analyse the technological and engineering needs of Big Data, focusing on large volumes (Volume) and right-time (Velocity) data management. This course is a natural continuation of ADB in the first semester and goes deeper in advanced data management techniques (i.e., NoSQL solutions) that scale with the infrastructure.
• Semantic Data Management (SDM, 6 ECTS, Prof. Oscar Romero). In this course, students will learn semantic-aware data management and modelling techniques, based on property and knowledge graphs, for dealing with highly heterogeneous (both syntactically and semantically) data sources (Variety) and automate, as much as possible, its integration in the presence of Volume and/or Velocity.
• Machine Learning (ML, 6 ECTS, Prof. Marta Arias). In this course, students will learn the development of theories, techniques and algorithms to allow a computer system to modify its behavior in a given environment through inductive inference. The goal is to infer practical solutions to difficult problems –for which a direct approach is not feasible– based on observed data about a phenomenon or process. This course introduces the students to fundamental knowledge in the field of Machine Learning to enable them conduct rigorous data analysis.
• Viability of Business Projects (VBP, 6 ECTS, Prof. Marcos Eguiguren). In this course, students will learn the business and entrepreneurial aspects of Big Data. They will learn how to analyse the viability of data-intensive new business ventures or assess the value or a new project, by developing the capacity to identify opportunities, validate them, and draft a realistic plan. This course covers the business aspect of Big Data.
• Big Data Seminar (BDS, 2 ECTS, Prof. Oscar Romero). In this seminar, students will get a view of recent trends in Big Data. Lectures given by consortium partners and guest speakers will present business cases, research topics, internships and Master’s thesis subjects, and the motivation behind the three specialisations. Students will also perform an autonomous state-of-the art research in an advanced topic not covered during the semester, which will be presented and jointly evaluated by all partners during the summer school.
• Humanities: Foreign Language (FL2, 2 ECTS, Dept. of Terminology and Language Services). This course, adapted to students’ proficiency, will introduce the students either to Spanish or Catalan language.
• Humanities: Debates on Ethics of Big Data (DEBD, 2 ECTS, Prof. Alberto Abelló). This course fosters the social competences of students by introducing them to concrete problems involving data-related ethical issues through debates that aim at building their critical attitude and effective communication and reflection. A written summary of their position is meant to train their writing skills.

Students will attend the summer school organised annually by one partner institution. Presented by leading researchers in the field, it provides students with theoretical and practical skills in the domain. Industrial presentations will allow participants to understand the current product offer.

More information on the summer school can be found in the Summer School page.

Although not mandatory, in order to acquire a first working experience, students are encouraged to participate in summer internships, typically with industrial associated partners, between the end of the summer school and the beginning of the third semester.

The specialisation focuses on methods, techniques, and tools for the design and analysis of process-aware business information systems, i.e., systems that support business processes in organisations. The objective is that students are able to build complex systems involving processes, humans, and organisations, thus dealing with the Variety and the Value challenges. The specialisation consists of the following courses.

• Foundations of Process Mining (FPM, 5 ECTS, Prof. Boudewijn van Dongen). The course covers the fundamentals of process mining. We start with data pre-processing and we show how through a visual inspection of event data and filtering, data can be pre-processed. In a second step, we consider the problem of process discovery, i.e. how to obtain a process model from an event log automatically. We again focus on the basics, but also show state-of-the-art in this area. To assess the quality of the discovered models, we show the fundamentals of conformance checking, i.e. the comparison of event logs and process models. We discuss the four main quality dimensions: fitness, precision, generalization and simplicity.
  Finally, we show how the event log can be combined with the model to enrich the model with other perspectives, such as performance, decision points and resource information.
  The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the data science field.
• Responsible Data Challenge (RDC, 5 ECTS, Dr. Dirk Fahland). In the course, students will work in groups using SCRUM and follow the complete CRISP-DM lifecycle and use data exploration and visualization to gain an understanding of the data and acquire domain knowledge to derive clearly formulated research questions suitable for the stakeholder, develop and conduct an analysis that can handle various dimensions of the data and the research question, and validate their findings both technically as well as through visualizations adequate for stakeholders.
• Seminar Process Analytics (SPA, 5 ECTS, Dr. Marwan Hassani). In this seminar, we will study and discuss the state-of-the-art research on Process Mining in the context of practically relevant uses cases from industry. We will discuss works along the entire process mining life-cycle: event data extraction and event log construction, various event data pre-processing methods, process model discovery, conformance checking and deviation detection, prediction based on models learned from event data, and visualization techniques. Additionally, students will be exposed to data mining and machine learning solutions that are relevant for process mining problems. This provides students a good insight into this field of research in preparation for a Master project in the PA group.
• Advanced Process Mining (APM, 5 ECTS, Dr. Dirk Fahland). This advanced course on process mining teaches students the fundamental concepts and theoretical foundations of process mining along a complete process mining methodology, and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension. The course material is based on recent research articles in the field and the course teaches students how to read and understand research literature.
• Graduation Preparation (GP, 10 ECTS). At the end of the Graduation Preparation the student is able to:​ summarize and discuss the problem context, domain-knowledge, and the requirements these set; demonstrate an understanding of and discuss the relevant state-of-the-art in research and other prior work in academia (and the organization); formulate one or more research questions relevant with respect to the given context and state-of-the-art​; establish and discuss a project plan with the necessary steps of a research project to answer this research question​; describe and discuss the intended research methodology and the steps for evaluating whether the research question was answered successfully and/or the proposed solution is efficacious​; identify and mitigate risks in conducting this research project with the given plan, and discuss risks and mitigation plans​.

This specialisation focuses on models, algorithms, and technologies related to decision-support systems and massive data analytics. The specialisation covers theoretical foundations such as decision-making in uncertain situations, advanced machine learning, graph management and analytics as well as visualisation and innovation. The specialisation is composed of the following courses.

• Decision Modelling (DeM, 5 ECTS). This course aims at presenting classical decision models with a special emphasis on decision making in uncertain situations, decision with multiple attribute, and decision with multiple stakeholders. During the course, various applications will be presented, emphasizing the practical interest and applicability of the models in real-world decision situations.
• Machine Learning (AML, 5 ECTS). The goal of this course is to provide the student with knowledge about deep and reinforcement learning paradigms; the mathematical foundations and practices of different variants of machine learning methods.
• Visual Analytics (VA, 5 ECTS). This course aims to provide the student with knowledge about the multidisciplinary field of Visual Analytics, the foundations to build visual analytics systems using real-world data and to familiarise with current technologies.
• Massive Graph Management & Analytics (MGMA, 5 ECTS). The objectives of this course is to provide the student with knowledge about designing high-performance and scalable algorithms for massive graph analytics. The course focuses on modeling and querying massive graph data in a distributed environment, designing algorithms, complexity analysis and optimization, for massive data graph problem analytics.
• Big Data Research Project (BDRP, 5 ECTS). This course aims at preparing the students for the master thesis of the 4th semester. The students will learn how to manage a research project related to massive and heterogeneous data management and analytics from scratch, working in a team, and using all the steps required in a scientific methodology. During this course the students will attend seminars in order to have a better understanding of research methodologies and to be aware of some ongoing research projects presented by researchers.

• Law & Intellectual Property (BIM, 2.5 ECTS). The objectives of this course are to provide the student: (i) knowledge about intellectual and industrial properties, data protection and security in European research context, (ii) an overview about current and innovative company projects and technology needs for real data analytics and machine learning.
• French Language and European Culture (FLE, 2.5 ECTS)

During the fourth semester, students will put into practice what they have learned during the previous semesters, either in an industrial or a HEI partner. Students are encouraged to devote their master’s thesis to start-up creation. The thesis is evaluated jointly. The thesis work will be considered for submission to scientific conferences.

The closing event of the programme is organised annually by one partner institution. All main partners will participate in the event, associated partners and industrial organisations will be invited to attend. In this event the students will defend their master’s thesis, which will allow all partners to evaluate their skills. The event will also be the ideal place to assess the programme, and to discuss best practices and curriculum evolution. The event will be followed by the graduation ceremony.

Please view the detailed course description.