This unit module provides the techniques for describing, visualising and understanding data. It also provides the mathematical tools necessary for the other units of the Data Science major.
| On completion of the unit, the student will be capable of: | Classification level | Priority |
|---|---|---|
| Knowing the fundamentals of data science (definition, theorem, proof, terminology | 1. Knowledge | Essential |
| Recognizing the nature of the exploration problem | 2. Understand | Essential |
| Formulating a problem | 2. Understand | Important |
| Applying mathematical tools | 3. Apply | Essential |
| Testing and analysing results | 4. Analyse | Essential |
| Interpreting results | 5. Summarise | Essential |
| Presenting and communicating on the technical analysis of results | 6. Assess | Important |
| Percentage ratio of individual assessment | Percentage ratio of group assessment | ||||
|---|---|---|---|---|---|
| Written exam: | 30 | % | Project submission: | 0 | % |
| Individual oral exam: | 0 | % | Group presentation: | 0 | % |
| Individual presentation: | 0 | % | Group practical exercise: | 40 | % |
| Individual practical exercise: | 30 | % | Group report: | 0 | % |
| Individual report: | 0 | % | |||
| Other(s): 0 % | |||||
| Type of teaching activity | Content, sequencing and organisation |
|---|---|
| Course | Generalities: question of missing data, detection of outliers and robust methods, descriptive statistics Course and practical work sessions with a case study – computer application |
| Course | Visualisation and dimension reduction: analysis of principle components, discriminatory descriptive analysis. Factorial analysis of similarities Course and practical work sessions with a case study – computer application |
| Course | Gaussian vector statistics. Course and practical exercises, supervised studies |
| Course | Combinatorial optimisation: classical methods Course and practical work sessions with a case study – computer application |