Learning outcomes

dats2m  2017-2018  Louvain-la-Neuve

Acquire robust methodological bases in analysis and data processing and apply them in varied domains such as human sciences, engineering, marketing, finance, insurance, or scientific research.

After completing the training, the student will master the fundamental concepts in statistics, algorithmic, data mining, and machine learning that are necessary for the job of «data scientist». He will develop skills in communication and will be capable of analyzing a complex problem, of collaborating in a research project. According to the objectives aimed by the student, several elective modules are proposed: applied data, dated sciences in linguistics, algorithmic and computing, statistics and sampling, dated sciences applied to management.

On successful completion of this programme, each student is able to :

1. Demonstrate the control of a robust corpus of knowledge in data sciences, allowing him(her) to solve the problems which are a matter of his(her) discipline

1.1. The structures of data and algorithms for the analysis of data.
1.2. The theories of the learning, the data mining and the visualization of large-dimension data.
1.3. The statistical inference, the modelling and statistical computing.
The student in the orientation information technologies specializes via compulsory or electives courses.
1.4. The industrial and entrepreneurial aspects of data sciences.
1.5. The computer systems, including parallel computing, the networks and the safety(security).
1.6. Numerical methods and optimization, constrained optimization included, operational research, identification and applied mathematics.

2. Organize and to lead to its term an initiative of development of a data operating system, fulfilling to complex needs of a customer.

2.1. Analyze the problem or solving the functional needs and to formulate the corresponding specifications.
2.2. Formalize and model the problem and design one or several original technical solutions answering these specifications.
2.3. Estimate, justify and classify the solutions with regard to all the criteria appearing in technical specifications: efficiency, feasibility, quality, relevance and security.
2.4. Implement, test and validate the selected solution and interpret the results.
2.5. Formulate recommendations to improve the operational features of the solution.

3. Organize and lead to his term a research work to comprehend an unsolved problem bound to the exploitation of data according to a new methodology or in a new environment.

3.1. Document and summarize the state of the current knowledge in the considered domain.
3.2. Propose a modelling and/or an experimental plan allowing to simulate and to test hypotheses relative to the studied problem.
3.3. Shape a summary report to describe the methodology with rigor and clarify the theoretical and\or technical potentialities of innovation resulting from this research work.

4. To contribute in team to the conduct of a project of data exploitation and to lead it to its term by taking into account objectives, assigned resources and constraints that characterize it.

4.1. To center and clarify the objectives of a project (by associating it performance indicators) considering the stakes and the constraints that characterize the environment of the project.
4.2. To be collectively committed on a work plan, a schedule and roles.
4.3. Work in a multidisciplinary environment, together with other actors having various points of view: manage points of disagreement or conflicts.
4.4. To make decisions in team when there are choices: whether it is on the technical solutions or on the organization of the work to run the project successfully.

5. Communicate effectively orally and in writing to bring to a successful conclusion the projects which are entrusted to him (her) in his (her) working environment (in particular in English).

5.1. Identify clearly the needs for the "customer" or for the user: question, listen and understand all the dimensions of his request and not only the technical aspects
5.2. Argue and to convince by adapting itself to the language of his (her) interlocutors: technicians, colleagues, customers, managers.
5.3 Communicate under graphic and schematic shape; interpret a plan, present the results of a work, structure information.
5.4. Read, to analyze and to exploit technical documents (diagrams, textbooks, projects specifications).
5.5. Draft written documents by taking into account contextual requirements and social conventions on the subject.
5.6. Make a convincing oral presentation by using the modern techniques of communication.

6. Show at the same time rigorous, open, critical mind and ethics in its work.

6.1. Apply existing standards in the disciplines of data sciences (terminology, quality measures).
6.2. Find solutions which go beyond the strictly technical issues, by integrating the stakes in ethical dimension of a project (including the data privacy and the protection of the private life) and of sustainable development.
6.3. Show critical mind towards a technical solution to verify the robustness and to minimize the risks that a solution presents with regard to its implementation.
6.4. Make a self-assessment and to develop in an autonomous way the necessary knowledge to remain competent in his (her) domain.