 | Study programme | Français | |
 | Statistical Data Analysis | ||
Programme component of Bachelor's Degree in Engineering à la Faculty of Engineering |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| UI-B3-IRCIVI-311-M | Compulsory UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 30 | 6 | 0 | 0 | 0 | 3.00 | 3.00 |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-MARO-014 | Statistical Data Analysis | 30 | 6 | 0 | 0 | 0 | Q1 | 100.00% |
| Unité d'enseignement | ||
|---|---|---|
 | UI-B1-IRCIVI-003-M Mathématique pour l'ingénieur 1 | |
| UI-B2-IRCIVI-003-M Probabilité et Statistiques | |
Objectives of Programme's Learning Outcomes
- Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
- Identify, describe and explain basic scientific and mathematical principles
- Identify, describe and explain the basic principles of engineering particularly in their specialising field
- Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines
- Demonstrate thoroughness and independence throughout their studies
- Develop their scientific curiosity and open-mindedness
Learning Outcomes of UE
- understand and explain the theory, models and techniques used
- identify which model(s) are best suited for a given dataset
- analyse datasets using a software
- interpret the results from the software, showing an understanding of the theory
Content of UE
- descriptive techniques such as principal components analysis and discriminant analysis
- classical models of statistical data analysis (analysis of variance, linear regression)
- data mining (classification and clustering)
Prior Experience
- probability and statistics - algebra
Q1 UE Assessment Comments
Not applicable
Q2 UE Assessment Comments
Not applicable
Q3 UE Assessment Comments
Not applicable
Q1 UE Resit Assessment Comments (BAB1)
Not applicable
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-MARO-014 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-MARO-014 |
|
Required Reading
| AA | |
|---|---|
| I-MARO-014 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-MARO-014 | - lecture notes and problem sets - slides |
Recommended Reading
| AA | |
|---|---|
| I-MARO-014 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-MARO-014 | Sans objet |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-MARO-014 | R.O.Duda, P.E.Hart, D.G.Stork. "Pattern Classification". John Wiley and Sons, 2000. I. H. Witten, E. Frank. Data Mining : "Practical Machine Learning Tools and Techniques with Java Implementations". Morgan Kaufmann, 2010 J-M. Azaïs, J-M. Bardet, "Le Modèle Linéaire par l'exemple : Régression, Analyse de la Variance et Plans d'Expériences. Illustrations numériques avec les logiciels R, SAS et Splus", Dunot, 2006 R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, 2012 |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| I-MARO-014 | Autorisé |