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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. Toon Calders). 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.
  • Business Process Management (BPM, 5 ECTS, Prof. Toon Calders). 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.
  • Data Mining (DM, 5 ECTS, Prof. Toon Calders). 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.
  • Advanced Databases (ADB, 5 ECTS, Prof. Esteban Zimanyi). 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.
  • 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.
  • Humanities: Foreign Language (FL1, 2.5 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.
  • Humanities: Effective Communication (EC, 2.5 ECTS, Prof. Esteban Zim ́anyi). In this course, students will acquire a clear insight into oral and written communication and will understand how communication works and how to effectively communicate with self-confidence and intuition. Students will assess their way to communicate and will experience tools and techniques to help them improve their communication skills.

 

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 Abello). 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.

 

 

Content and Usage Analytics (UFRT, 3rd semester, 30 ECTS)

This specialisation focuses on three of the Vs of BD, namely Variety, Variability, and Veracity. Its objective is to train students to understand that accurate analysis depends on the quality and integration of highly heterogeneous source data. Students will get acquainted with a range of data types and sources including textual, web, spatio-temporal data and user traces, along with the techniques to process and analyse them. The specialisation is composed of the following courses.

  • Data and Knowledge Quality (DKQ, 6 ECTS, Prof. Veronika Peralta). In this course, students will learn the concepts and techniques for assessing and assuring the quality of data and knowledge, by deeply studying the key quality dimensions.
  • User-Centric Approaches (UCA, 5 ECTS, Prof. Patrick Marcel). In this course, students will learn the models and approaches needed for user-centric data analysis, by studying preference models and processing, query personalisation, recommender systems, and gain experience with platforms like Apache Mahout.
  • Natural Language Processing (NLP, 5 ECTS, Prof. Agata Savary). In this course, students will learn the key techniques for analysing textual data. These include named entity recognition, entity linking, mul- tiword expressions, shallow and deep parsing, information extraction, ontology extraction and enrichment.
  • Advanced Data Mining (ADM, 6 ECTS, Prof. Arnaud Giacometti). In this course, students will learn advanced machine learning and data mining techniques related to the variety of data, focusing on link analysis, social networks analysis, preference mining, usage analysis, trends detection, and sentiment mining.
  • Content and Usage Analytics Seminar (CUAS, 5 ECTS, Prof. Nicolas Labroche). In this seminar, students will get in touch with research in the area, by following lectures by guests and experts, studying research papers, and writing scientific publications, preparing them for their master’s thesis.
  • Humanities: Ethics and Digital Technologies (EDT, 3 ECTS, Prof. Jean-Yves Antoine). In this course, students are introduced to the ethical implications of digital technologies, covering in particular philosophical foundation of ethics and current ethical approaches, and an overview of ethical issues in BD.
    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.<.p>

 

Please view the detailed course description.

 

 


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