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. Dimitris Sacharidis). In this course, students will learn the basic concepts for modelling and implementing business processes using contemporary information technologies and standards, such as BPMN.
    • Database Systems Architecture (DBSA, 5 ECTS, Prof. Mahmoud Sakr). 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 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.

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.

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 Data Analytics (CentraleSupelec, 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. Brice Mayag). 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. Michelle Sebag). 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, 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, N. Seghouani and S. Maniu). 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. N. Seghouani with the participation of other researchers. 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, 2.5 ECTS, 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.
  • French Language and European Culture (FLE, 2.5 ECTS)

Statistics and Deep Learning for Data Analytics (uniPD, 3rd semester, 30 ECTS)

The proposal of specialization aims to provide students with advanced Data-Science methods and strengthen their background in Statistics and Deep Learning. In this respect, University of Padua will provide a first mandatory course on Statistical Inference, and a second mandatory course on Deep Learning. The latter course also focuses on the application of Deep Learning techniques on the analysis of human data. Furthermore, students with a strong interest in statistical methods can opt to further enlarge their background of Data-Science methods by also taking a course on Stochastic Model.

While fortifying their background on Data-Science methods, the specialization puts a strong accent on the applications to Data Science. In particular, students will be able to learn the diverse background knowledge that enables them to carry on data-science projects in different domains, and to extract and understand the results that are being obtained.

In particular, students (1) will learn how to conduct an analysis of data that originate from different contexts, ranging from human and economic data, till biological data, and (2) will acquire the knowledge of these domains to be able to interpret the results.

The specialization will offer the following mandatory courses (18 ETCS):

  • Statistical Learning (6 ETCS, Prof. Roverato). The students will familiarize with statistical thinking, gain adequate proficiency in the development and use of standard statistical inference tools. The course will largely focus on the definition of estimators (maximum likelihood estimation, accuracy of estimation, the sampling distribution of an estimator) and on the hypothesis testing. During the course, the students will learn how to use the R tool to conduct a statistical analysis of datasets.
  • Deep Learning and Human Data Analytics (6 ETCS, Prof. Rossi).The students will familiarize with advanced machine-learning and deep-learning techniques: Vector Quantization, Hidden Markov Models, Deep Neural Networks (Feed Forward Neural Networks, Convolutional Neural Network). The course will discuss the application for the analysis of human data: ECG signals, natural language processing, facial data, behavioral data. Students will be able to experience the use of deep-learning techniques and the application to human data in 6 laboratory sessions of two hours.
  • Time-Series Analysis for Business Economic and Financial Data (6 ETCS, Prof. Guidolin). This course aims to introduce the students to the main statistical features and concepts underlying the analysis of data collected over time, as well as providing the basic statistical solutions to analyze such data in economic, financial and business settings. Some case studies will be described to allow students to learn the aspects to consider to analyze economic and financial time-series data. Students will also learn how to analyze business and marketing data analyses through dynamic modelling.

The students will have to attend two additional courses (12 ETCS) that they can freely select among the following:

  • Law and Data (6 ETCS, Prof. Spiller). The course will enable students to understand the legal components and to predict the legal consequences of data-science activities. Students will reflect on the most controversial and contemporary legal issues of data analysis
  • Stochastic Methods (6 ETCS, Prof. Ferrante). This course is to introduce techniques to analyze Stochastic Processes: Markov chains and random walks, Montecarlo Simulations, Random Graphs (Erdos-Renyi, Random Regular and Dynamic Graphs). Using the software R, specific problems will be approached via computer simulation.
  • Biological Data (6 ETCS, Prof. Tosatto). The course intends to communicate basic knowledge on different types of biological data, such as sequence, structure, network and literature. Moreover, it intends to enable the student to autonomously develop a research project in bioinformatics, defining the state of the art for an open problem and providing an attempt to solve it through the extension of existing software libraries and the critical evaluation of obtained results.

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.