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Business Intelligence Fundamentals (ULB, 1st semester, 30 ECTS)

This semester introduces essential topics in BI, covering data warehouses, data mining, and business processes, as well as essential aspects of data management, covering traditional relational technology and new emerging paradigms. It is composed of the following courses.
 

  • Data Warehouses (DW, 5 ECTS, Prof. Esteban Zimányi). In this course, students will learn the concepts and techniques necessary for designing, implementing, exploiting, and maintaining data warehouses. This includes multidimensional databases and data warehouses, OLAP, reporting, and ETL processes.
  • Data Mining (DM, 5 ECTS, Prof. Mahmoud Sakr). In this course, students will acquire the basic concepts of data mining. In particular, the course focuses on the strengths and limitations of popular data mining techniques, as well as their associated computational complexity issues.
  • Business Process Management (BPM, 5 ECTS, Prof. Mahmoud Sakr). In this course, students will learn the basic concepts for modelling and implementing business processes using contemporary information technologies and standards, such as BPMN and BPEL.
  • Database Systems Architecture (DBSA, 5 ECTS, Prof. Stijn Vansummeren).
    In this course, students will acquire a fundamental insight into the implementation of database systems. The course analyses the internals of relational database management systems, focusing on query and transaction processing.
  • Advanced Databases (ADB, 5 ECTS, Prof. Esteban Zimányi). In this course, students will learn the concepts and techniques of some innovative database applications, including NoSQL and NewSQL databases, management of nontraditional data such as spatial or temporal data, as well as data governance.
  • Humanities: Foreign Language (FL1, 5 ECTS, Fondation 9 Languages co-organised by ULB). French course adapted to the students’ proficiency. Those whose mother tongue is French will be enrolled in a Spanish, Dutch, or German course, covering the languages of the second semester and the specialisations.

Big Data Fundamentals (UPC, 2nd semester, 30 ECTS)

This semester focuses on basic concepts of distributed systems and BD management, aiming to train students in understanding how data management can scale to large volumes while potentially also dealing with velocity and variety. In particular, NoSQL databases and semantic data management will be at the core of the semester, together with other topics needed in preparation to the specialisations. Students, divided in teams, define and implement their own 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 BD, by continuing ADB in the first semester and going 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 (i.e., graph and semantic web) for tackling Variety in combination with Volume and/or Velocity.
  • Cloud Computing (CC, 6 ECTS, Prof. Angel Toribio). In this course, the students will learn the principles and the state of the art of large-scale distributed computing in a service-based model. They will look at how scale affects system properties, models, architecture, and requirements.
  • Viability of Business Projects (VBP, 6 ECTS, Prof. Marcos Eguiguren). In this course, students will learn the business and entrepreneurial aspects of BI/BD. They will practice analysing the viability of new business ventures, developing the capacity to identify opportunities, validate them, and draft a realistic plan.
  • Big Data Seminar (BDS, 2 ECTS, Prof. Oscar Romero). In this seminar, students will get a view of recent developments in BI/BD. 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 a state-of-the art research in one of the topics, which will be presented and jointly evaluated by all partners in the summer school.
  • Humanities: Foreign Language (FL2, 2 ECTS, Dept. of Terminology and Language Services). This, adapted to students’ proficiency, will introduce them to Spanish (Catalan for native Spanish speakers).
  • Humanities: Social and Ethical Impact of Big Data (SEIBD, 2 ECTS, Prof. Alberto Abell ́o). This course fosters the social competences of students, by introducing them to concrete problems involving ethical issues in BD 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.

European Business Intelligence and Big Data Summer School (Summer after the 2nd semester)

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.

Summer Internship (Summer after the 2nd semester)

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.

Large-Scale Analytics (TUB, 3rd semester, 30 ECTS)

This specialisation focuses on scalable data analytics for BI, particularly, on large, heterogeneous, and high-throughput data (i.e., for both data-at-rest and data-in- motion). The theoretical courses enable students to acquire a foundation on large-scale analytics addressing the Volume, Velocity, and Variety challenges. In addition, the seminar on the state-of-the-art scalable analytics and tools, and the project offer practical experience for students to gain expertise in the usage of open-source BD tools. The specialisation consists of the following courses (students must choose two among the first three).
 

  • Scalable Data Science (SDS, 6 ECTS, Prof. Volker Markl). This course introduces various parallel processing paradigms. Students will learn how to adapt standard algorithms for data mining, machine learning, text mining, graph analysis, and recommender systems to scalable processing paradigms.
  • Management of Heterogeneous Information (MHI, 6 ECTS, Prof. Ralf Kutsche). In this course students will learn concepts, methods, and tools for extracting and integrating large amounts of heterogeneous information. Students will experiment with scalable implementations of these concepts.
  • Management of Data Streams (MDS, 6 ECTS, Prof. Volker Markl). In this course students will acquire the theory and practical experience for analysing data streams, including windowing operations and data stream mining. They will experiment the combined analysis of data in motion and data at rest.
  • Big Data Analytics Project (BDAP, 9 ECTS, Prof. Volker Markl). In this course students will learn to systematically analyse a current issue in the information management area and to develop and implement a problem-oriented solution as part of a team.
  • Big Data Analytics Seminar (BDAS, 3 ECTS, Prof. Volker Markl). This seminar covers recent results and trends in the analysis of large-scale data. Students will learn the comprehensive preparation and presentation of a research topic in this field, by conducting literature review.
  • Humanities: Interdisciplinary Communication (IC, 6 ECTS, Dr. Achim Goeres). In this course students will learn how to improve their presentation and teamwork skills by learning about role identification and group dynamics, as well as the ethical dimensions of interdisciplinary and intercultural work. Further, students can follow German courses at various levels and specialised courses for developing horizontal skills. The participation to one German course is free and implies the delivery of a certificate.

Business Process Analytics (TU/e, 3rd semester, 30 ECTS)

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.
 

  • Business Information Systems (BIS, 5 ECTS, Prof. Wil van der Aalst). In this course students will learn about the modelling, analysis, and enactment of business processes and the information systems to support these processes, understanding the relationship between systems and processes.
  • Introduction to Process Mining (IPM, 5 ECTS, Prof. Wil van der Aalst). In this course students will acquire the theoretical foundations of process mining and will be exposed to real-life data sets helping them understand the challenges related to process discovery, conformance checking, and model extension.
  • Visualisation (VIS, 5 ECTS, Prof. Dirk Fahland). In this course students will learn the theory and practice of data visualisation, including topics such as data representation, grid types, data sampling, data interpolation, data reconstruction, datasets, and the visualisation pipeline.
  • Statistics for Big Data (SBD, 5 ECTS, Prof. Edwin van den Heuvel). In this course students will learn various statistical methods for analysing BD, focusing on analysing temporal observational data, i.e., data that is collected over time without involving well-developed experimental designs.
  • Business Process Analytics Seminar (BPAS, 5 ECTS, Prof. H.A. Reijers). This seminar introduces students to research in business process analytics by following lectures by staff members and guest lecturers from industry, analysing master theses, study research papers, and execute a small research project.
  • Humanities: Ethics of Technology (ET, 5 ECTS, Prof. A.J.K. Pols) This course enables students to analyse ethical questions related to the design and use of new technology, and its implication for human beings, society, and the environment.
    In addition to these courses, students can follow Dutch courses at a variety of CEFR levels.

Decision Support and Analytics (CS, 3rd semester, 30 ECTS)

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 Modeling (DeM, 5 ECTS, Prof. Vincent Mousseau). 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.
  • Advanced Machine Learning (AML, 5 ECTS, Prof. Antoine Cornuéjols). The goal of this course is to provide the student with knowledge about supervised, unsupervised and reinforcement learning paradigms; the mathematical foundations and practices of different variants of machine learning methods.
  • Visual Analytics (VA, 5 ECTS, Prof. Petra Isenberg). 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, Profs. Nacéra Seghouani Bennacer, Francesca Bugiotti). 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, Profs. Nacéra Seghouani Bennacer, Francesca Bugiotti). 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.
  • Business Innovation Management (BIM, 5 ECTS, Prof. Prof. Karim Tadrist and invited speakers). 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.
    Furthermore, French language courses for foreigners will be available for free, upon request.

Master’s Thesis (4th semester, 30 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.

Final event (Summer after the 4th semester)

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.

 

 


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