 | Study programme 2020-2021 | Français | |
 | Statistical Data Analysis | ||
Programme component of Master's in Mathematics à la Faculty of Science |
| Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
|---|
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-042-M | Optional UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 18 | 18 | 0 | 0 | 0 | 4 | 4.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-MARO-014 | Data Mining | 18 | 18 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Have integrated and elaborate mathematical knowledge.
- Mobilise the Bachelor's course in mathematics to address complex issues and have profound mathematical expertise to complement the knowledge developed in the Bachelor's course.
- Use prior knowledge to independently learn high-level mathematics.
- Read research articles in at least one discipline of mathematics.
- Carry out major projects.
- Appropriately use bibliographic resources for the intended purpose.
- Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
- Mobilise knowledge, and research and analyse various information sources to propose innovative solutions targeted unprecedented issues.
- Communicate clearly.
- Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
- make a structured and reasoned presentation of the content and principles underlying a piece of work, mobilised skills and the conclusions it leads to.
- Adapt to different contexts.
- Have developed a high degree of independence to acquire additional knowledge and new skills to evolve in different contexts.
- Critically reflect on the impact of mathematics and the implications of projects to which they contribute.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
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
Elementary statistics
Algebra and Calculus
Type of Assessment for UE in Q1
- Written examination
- Practical test
Q1 UE Assessment Comments
written exam for the theory, followed by a practial exam on the computer, with oral interrogation
Type of Assessment for UE in Q3
- Written examination
- Practical Test
Q3 UE Assessment Comments
written exam for the theory, followed by a practial exam on the computer, with oral interrogation
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
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 | - slides of oral presentations (theory and examples) - problem sets |
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 | Authorized |