Of Science (MSc) in Data Science

Programme content

Data science

An increasing part of human, technological and scientific activities generate digital traces in the form of computer data. These data are produced at a high rate, accumulated in large volumes and come from various sources, in various formats ranging from strict structures used in databases to completely free text or image formats.

In this context, data science combines the logic of programming and inferential reasoning through computer science and statistics to describe, analyze and model these data. It allows to understand them, to extract new knowledge and to help decision making under uncertainty. Data science is a multidisciplinary field that includes a set of scientific methods, theories and algorithms, such as probabilistic modeling, machine learning and artificial intelligence, computer programming, data engineering, high performance computing, communication or visualization.

The data scientist, a versatile profile

A data scientist is a specialist in digital technology and data processing. He or she has a high level of scientific expertise that allows him or her to interact in many fields of activity, such as technology (e.g., computer science, telecommunications), science (e.g., environment, space, health), industry (e.g., agriculture, automotive, e-commerce), business, or society (e.g., politics, media).

THE PROGRAMME

The data science program offers a complete training to the future data scientist. At ULiège, the program is built around artificial intelligence and its mathematical and computational foundations and is complemented by courses aimed at addressing and practicing the other fundamental skills for a data scientist. The training integrates solid theoretical foundations and their practical implementation. This training also takes into account non-technical aspects that may influence the design, development and maintenance of the data science project such as ethics, legislation, or data governance. These aspects are either addressed in the technical courses or are the subject of courses organized by the relevant faculties. The master's thesis is also an opportunity to demonstrate and refine all of these skills.

The program is structured around :

• a core curriculum consisting of courses in computer science and statistics (probabilistic modeling, machine learning, artificial intelligence, numerical computation, databases) and cross-disciplinary courses (management, law) ;
• advanced elective courses in computer science, statistics, and several fields of expertise (bioinformatics, oceanography, geographic information systems, finance, etc.);
• a master's thesis in a research laboratory or company.

The master of science in data science engineering and the master in data science programs have strictly identical contents. The wording of the degree obtained depends solely on the initial training. The title of engineer in data science is reserved for holders of an undergraduate degree in engineering sciences.

Learning outcomes

I. Understand and be able to apply sciences and concepts within the field of engineering

Engineers master and are able to apply fundamental concepts and principles of various fields of science and technology. 

I.1 Master the concepts, principles and laws of the basic sciences (mathematics, physics, chemistry, computer science, etc.).

I.2 Master the concepts and principles of the engineering sciences. In particular, demonstrate a strong knowledge of the field of data science, including its mathematical foundations in probability theory, statistics, stochastic processes and systems analysis, and the advanced areas of numerical optimisation, machine learning, and artificial intelligence.

I.3 Master certain concepts and principles of computer science, including algorithms and programming, and its theoretical foundations.

II. Learn to understand

Engineers have a strong capacity for autonomous learning, which enables them to seek out and appropriate relevant information to address emerging issues and to engage in continuous learning.  They may also engage in research to advance the state of understanding.

II.1 Demonstrate autonomy in learning. In particular, know how to appropriate and summarise scientific and technical information from various sources (lectures, literature, references, manuals and technical documentation, online resources, etc.).

II.2 Research, evaluate and use (through scientific literature, technical documentation, the web, interpersonal contacts, etc.) new information relevant to understanding a problem or a new issue.

II.3 Carry out fundamental or applied research work to produce original scientific and technical knowledge.

III.  Analyse, model and solve complex problems

Engineers are capable of conducting structured scientific reasoning, demonstrating the capacity for abstraction, analysis and management of the constraints necessary to solve complex and/or original problems, thus forming part of an innovative process.

III.1 Formalise, model and conceptualise a scientific or technical problem related to or inspired by a complex real-life situation in rigorous language, e.g. using mathematical or computer language, to obtain results. Be capable of abstraction.

III.2 Critically analyse hypotheses and results and compare them with experimental reality, taking into account uncertainties.

III.3 Identify and manage the constraints associated with a project (technical constraints, specifications, deadlines, resources, customer requirements, etc.). 

III.4 Innovate through the design, implementation and validation of new solutions, methods, products or services.

IV. Implement the methods and techniques in the field to design and innovate while adopting an engineering approach

Engineers implement the methods and techniques specific to their field of specialisation to develop engineering projects and ensure the achievement of specific objectives in their working environment.

IV.1 Use a numerical/computational approach to investigate a problem and test hypotheses or solutions. 

IV.2 Use an experimental approach to investigate a problem and test hypotheses or solutions. 

IV.3 Analyse, design, develop and test systems using the concepts of artificial intelligence, computer vision, robotics or knowledge representation.

IV.4 Design and conduct a statistical study using large volumes of data.

V. Develop their professional practice within the context of a company

Engineers are responsible members of society and the professional world. They integrate economic, social, legal, ethical and environmental constraints and challenges into their work. 

V.1 Integrate human, economic, social, environmental and legal aspects into their projects.

V.2 Position themselves in relation to the professions and functions of an engineer, taking into account ethical aspects and social responsibility. Adopt a reflective stance, both critical and constructive, with regard to their own way of acting, their approach and their professional choices. In particular, critically analyse the societal impacts of their projects, of data science, and of artificial intelligence in general.

V.3 Develop an entrepreneurial activity.

VI. Work alone or in groups

Engineers are able to work independently and collaborate within a group or organisation. They demonstrate responsibility, team spirit and leadership.

VI.1 Work independently.

VI.2 Work in a team. Make decisions together. Distribute work and manage deadlines. Manage tensions. Demonstrate leadership skills.

VI.3 Work in an environment with different hierarchical levels, different skill levels and/or different expertise.

VII. Communicate

Engineers are capable of communicating and sharing their technical and scientific approach and results in writing and orally. Their command of at least one foreign language enables them to work in an international context.

VII.1 Understand general and technical documents related to the professional practice of the discipline (plans, specifications, etc.).

VII.2 Write a scientific or technical report by structuring the information and applying the standards in place in the discipline.

VII.3 Present/defend scientific or technical results orally using the codes and means of communication appropriate to the audience and the communication setting.

VII.4 Understand and write general and technical documents in a foreign language.

VII.5 Understand and present a general or technical oral presentation in a foreign language.