Objectives of Programme's Learning Outcomes

• Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Demonstrate independence and their ability to work alone or in teams.
• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Pursue further training and develop new skills independently.
• Develop and integrate a high degree of autonomy to evolve in new contexts.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To be able to find useful information in a large dataset using data mining and machine learning tools , to analyze correctly biological data with time-dependencies and to analyse the spatial data. To be able to present results in a reproducible way (reports) and to use professional software in data science: R, RStudio, R Markdown, git.

UE Content: description and pedagogical relevance

The pedagogical material is available online: https://wp.sciviews.org. The chapters of this UE are: 

- Classification I - LDA, general principle, confusion matrice, metrics
- Classification II - corss-validation, AUC, k-nn, lvq, raport, random forest
- Classification III = svm, neural networks, initiation to deep learning
- Time series I - description, manipulation, acf, spectral analysis
- Time series II - decomposition & regularisation
- Spatial statistics, initiation, maps & krigging

Prior Experience

Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. General uni- and multivariate statistics, (generalized) linear models, nonlinear models, ACP & AFC, non supervised classification (hierarchical clustering and K-means). An update of the knowledge prior to the course can be done via the first two books of the data science courses available online at https://wp.sciviews.org.