At the end of this learning unit, the student is able to :
Having regard to the LO of the programme, this activity contributes to the development and acquisition of the following LO :
At the end of this course, students should be able to :
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Potential covered topics are (but not limited to): database design for data analytics, business intelligence techniques, dimensionality reduction for data visualization, extracting recurrent patterns from data, cluster analysis, predictive modeling (supervised classification and regression methods), modeling relationships by latent variable techniques, data analysis algorithms scaling to big data, etc. All these techniques will be illustrated through business applications.
Typically, these last years, the course was split into two parts: "Data management techniques" and "Machine learning techniques for supervised classification".
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Classical courses (either on-site or remotely, depending on the situation) and case studies
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Continuous evaluation
- Date: Will be specified later
- Type of evaluation: Project with report
- Comments: 60% of the final result
- Oral: No
Unavailability or comments: No
- Oral: No
- Written: Yes
- Comments: 40% of the final result
- Provost & Fawcett (2013), Data science for business. O Reilly.
- Sherman (2014), Business intelligence guidebook: from data integration to analytics. Morgan Kaufmann.
- Efraim, Sharda & Delen (2010), Decision support and business intelligence Systems. Pearson.
- Leskovec, Rajaraman & Ullman (2014), Mining of massive datasets, 2nd ed. Cambridge University Press.
- Kelleher, Mac Namee & D Arcy (2015), Fundamentals of machine learning for predictive data analytics. MIT Press.
- Hastie, Tibshirani & Friedman (2009), The elements of statistical learning, 2nd ed. Springer-Verlag.
- Izenman (2008), Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer.
- Bellanger & Tomassone (2014), Exploration de données et méthodes statistiques : data analysis & data mining avec le Logiciel R. Ellipses.