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.
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:
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.
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.
Curriculum’s courses. The specialization will offer the following mandatory courses during the third semester (i.e. the first semester of the second year):
Necessary background: Basic knowledge of any programming language, basic knowledge of probability theory, and linear algebra.
Description and Learning Objectives: 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. Further information
Necessary background: Basic knowledge of any programming language with a preference for Python, basic knowledge of probability theory, and linear algebra. Basic knowledge of Machine Learning.
Description and Learning Objectives: 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.Further information.
Note that, even if the course is indicated to be during the second semester, it has already been agreed that it will be moved to the first semester for the purpose of the BDMA specialization.
Necessary background: Probability Theory. Some basic background of statistics is necessary (hypothesis testing), but this will be briefly provided by the lecturer. Also, students will deepen their knowledge in that respect through the mandatory course “Statistical Learning”, which will run in parallel.
Description and Learning Objectives: This course aims at introducing 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. Further information.
The mandatory course will cover 18 ETCS of the specialization’s teaching module. To reach 30 ETCS, the students will have to attend two additional courses (12 ETCS) that they can freely select among the following:
Necessary background: None
Description and Learning Objectives: 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, including the European General Data Protection Regulation – GDPR. Further information
Necessary background: Basic notions of differential and integral calculus, linear algebra and probability theory.
Description and Learning Objectives: The aim of 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. Further information
Necessary background: Basic knowledge of optimization methods and machine learning. Python Programming Language.
Description and Learning Objectives: 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 structural 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. Further information
The course 4 (Law and Data) is meant for those students who are interest to investigate the legal and ethical repercussion of carrying on a data-science project.
The course 5 (Stochastic Method) is envisioned for those students who aim to strengthen the knowledge of statistical methods for a later application in a data-science project, e.g. during the final Master project (see the corresponding section below)
The course 6 allows students to familiarize with the analytics of biological data. The courses do not require any existing knowledge of biology: the important aspects will be covered in the course.
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.
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