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
The Master in Data Science has acquired highly specialized and integrated knowledge, as well as broad skills in various fields of Data Science and Computer Science, complementing those acquired during her/his bachelor of computer science, namely in algorithms, programming and computer systems (operating systems, data bases, networks).
- She/he has strengthened her/his knowledge in optimization and in various theoretical aspects of computer science (state machines, automata, grammars, theory of computation).
- She/he has mainly acquired highly specialized and integrated knowledge in data science and in artificial intelligence.
- She/he knows the fundamental theories and concepts of computer science, its types of reasoning, methodology and mathematical basis (graph theory, numerical methods, discrete optimization, information theory, state machines, automata, grammars, theory of computation, formal reasoning) and she/he has acquired a strong specialization in machine learning, statistics and artificial intelligence.
- She/he is able to apply and leverage her/his consolidated knowledge and skills to contribute, on her/his own or in a team, to plan, lead and carry out a large-scale big data or data science project while controlling its complexity and taking into account objectives, resources and specific constraints.
- She/he has acquired the highest degree of technical qualification allowing her/him to organize and carry out research, development or innovation work to understand a novel problem in her/his domain.
- Her/his solid grounding allows her/him to join a research and development team or start doctoral studies.
- She/he has become aware of the needs and constraints of the industrial reality, either by means of her/his master thesis or a company internship, or thanks to courses that establish links between the concepts learned and their industrial applications.
- She/he is able to communicate her/his conclusions, original propositions and the underlying knowledge and principles, in a clear, structured and reasoned way, adapting to the audience, both orally and in writing, particularly in English.
- She/he has developed and integrated a significant degree of autonomy, allowing her/him to acquire new knowledge, continue her/his training and develop new skills to be able to evolve in other contexts.
- She/he is well prepared to adapt to processes, techniques, languages, tools, ... that are yet to be invented. She/he has the capability to think critically about the societal impact of her/his projects and of data science and artificial intelligence in general.
- She/he shows rigor, autonomy, creativity, and a strong ethical sense.