 | Study programme 2018-2019 | Français | |
 | Data Science | ||
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-313-M | Compulsory UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Anglais, Français | 46 | 38 | 0 | 0 | 0 | 7 | 7.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-TCTS-030 | Signal Processing 1 | 16 | 32 | 0 | 0 | 0 | Q1 | 57.00% |
| I-MARO-014 | Data Mining | 30 | 6 | 0 | 0 | 0 | Q1 | 43.00% |
| Programme component | ||
|---|---|---|
 | UI-B1-IRCIVI-003-M Engineering Mathematics 1 | |
| UI-B2-IRCIVI-002-M Applied Mathematics | |
| UI-B2-IRCIVI-003-M Probability and Statistics | |
| UI-B2-IRCIVI-009-M Fields, Signals and Systems | |
Objectives of Programme's Learning Outcomes
- Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
- Identify and describe the problem to be solved and the functional need (of prospective clients) to be met considering the state of technology
- Identify and acquire the information and skills needed to solve the problem
- 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
- Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
- Present analysis or experiment results in laboratory reports
- Demonstrate thoroughness and independence throughout their studies
- Identify the different fields and participants in engineering
- Develop their scientific curiosity and open-mindedness
- Learn to use various resources made available to inform and train independently
Learning Outcomes of UE
- analyze various kinds of data and signals - understand the underlying theory for the development of basic components of a numerical signal processing system - implement these components with MATLAB - understand and explain the theory, models and techniques used for statistical data analysis - analyse datasets with a given software (MATLAB, R, Weka, ...) - interpret the results from the software, showing an understanding of the theory
Content of UE
- linear and invariant numerical systems; frequency analysis of numerical signals and systems; Shannon theorem and sampling; discrete Fourier transform; spectral analysis of random signals; numerical filters; simple systems under MATLAB - descriptive techniques for data analysis (principal components analysis, discriminant factorial analysis) - classical models of data analysis (analysis of variance, linear regression) - data mining (classification and clustering)
Prior Experience
Algebra, Analysis, functions of complex variables
Type of Assessment for UE in Q1
- Presentation and/or works
- Written examination
- Practical test
Q1 UE Assessment Comments
Weights : Signal processing : 4/7 Statistical Data Analysis : 3/7
Type of Assessment for UE in Q3
- Written examination
Q3 UE Assessment Comments
idem Q1
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
n/a
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-TCTS-030 |
|
| I-MARO-014 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-TCTS-030 |
|
| I-MARO-014 |
|
Required Reading
| AA | Required Reading |
|---|---|
| I-TCTS-030 | Note de cours - Traitement du Signal (1 et 2) - Thierry Dutoit |
| I-MARO-014 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-TCTS-030 | Not applicable |
| I-MARO-014 | - slides of oral presentations (theory and examples) - problem sets |
Recommended Reading
| AA | Recommended Reading |
|---|---|
| I-TCTS-030 | |
| I-MARO-014 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-TCTS-030 | Not applicable |
| I-MARO-014 | Sans objet |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-TCTS-030 | AUGER, F. (1999) Introduction à la théorie du signal et de l’information, 461 pp. Paris : TechnipDENBIGH, P. (1998) System Analysis and Signal Processing, 513 pp. Harlow : Addison-WesleyBAHER, H. (2001) Analog and Digital Signal Processing, 497 pp. Chichester : Wiley & SonsLYONS, R.G. (1998) Understanding Digital Signal Processing, 517pp. Harlow : Addison-Wesley |
| 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-TCTS-030 | Authorized |
| I-MARO-014 | Authorized |