1.1.1
M1 Curriculum
The M1 provides the fundamental tools in Computer Science and Mathematics necessary for the M2.
It is based on the three pillars characteristic to this master. As well as the fundamentals, students will
be taught the essential elements of project management. This first year will culminate in a large
transversal team project.
The M1 is divided into two semesters. Each semester is worth 30 ECTS.
Semester 1
Skills
Courses
Hours
Data
exploration
Inferential Statistics
30
Mathematics
for Computer
science
Software and
Architecture
Engineering
Science
Research
Initiation
Initiative
ECTS
5
Data Analysis
24
Partial Differential Equations and Finite Differences
30
Operational Research: Linear Optimization
20
Combinatory Optimization
18
Complexity theory
9
Object-Oriented Modelling (OOM) with UML
30
Object-Oriented Design and Programming with Java
30
Relational Database: Modelling and Design
30
Signal and System
21
3
Review a scientific paper
9
1
Personal and Professional Project
15
French as a Foreign language
40
8
Language
8
5
Total M1: Semester 1
306
30
Semester 2
Skills
Courses
Hours
ECTS
Mathematics
Simulation and Stochastic Process
30
10
for Computer
Science
Introduction to Predictive Modelling
21
Deterministic and Stochastic Optimization
30
Introduction to Data Mining
21
Signal processing
30
PLSQL
21
Architecture and Network Programming
30
Parallel Programming
30
Project
Management
AGIL Methods & Transverse Project
21
2
Final research
Final research project on BIG DATA
50
5
Language
French as a Foreign language
21
2
305
30
Engineering
science
Software and
Architecture
Total M1 : Semester 2
3
8
1.2 Data exploration
1.2.1
Inferential Statistics
Lecturer: Marietta Manolessou
1.2.1.1
Objective of the module
The aim of this course is to present the principles and technical tools of Inferential Statistics. More
precisely, by the end of the course the student will be able to analyze numerical data in large
quantities, for the purpose of reaching conclusions in probabilities, based on the data in a
representative sample. We study with the usual methods of estimations and tests.
The techniques introduced are illustrated via a series of EXCEL tutorials.
1.2.1.2
Topic in detail
•
Reminder on Probabilities (Random Variables, Random Vectors, Probability Distribution,
Functions, Independence and Dependence of random Variables - Conditional Probabilities and
Expectation values)
• Convergence, Limit theorems
1. Estimation
Proprieties of an estimator (Unbiased Estimator-Consistent and Efficient estimator)
(Examples – Exercises)
Usual estimators (Examples – Exercises)
Maximum Likelihood estimation (Examples – Exercises)
Estimation by interval of confidence (Examples – Exercises)
2. Hypothesis Testing
General principles (Examples – Exercises)
Test of a usual level of significance (Examples – Exercises)
Test of Variance (Examples – Exercises)
Usual tests of comparison (one and two samples) (Examples – Exercises)
Chi-square tests (Examples – Exercises)
1.2.1.3
Bibliography
• Probability Statistics and Queuing theory with Computer Science Applications, Arnold O.
Allen, Academic Press, 1990
• Maîtriser l’aléatoire, Eva Cantoni, Philippe Huber, Elvezio Ronchetti, Springer, 2006
• Mathematical Statistics with Applications, Kandethody, M. Ramachandran, Chris P. Tsokos,
Elsevier, 2009
• A Course in Mathematical Statistics, George G. Roussas, Academic Press, 1977
• Probabilités Analyse des données et statistique, G. Saporta, Editions Technip
• Tutorial by M. Manolessou
1.2.1.4
Websites
• http://sifoci.eisti.fr > Statistique : course webpage
• http://www-ljk.imag.fr/membres/Bernard.Ycart/codes/scilab.html#STAT : website featuring
the Scilab programs
• http://www.info.univ-angers.fr/~gh/Datasets/datasets.htm : website featuring files
containing statistics studies data
• http://www.agro-montpellier.fr/cnam-lr/statnet/ : website featuring online classes
• http://www.modulad.fr/ : website of the free press review Modulad (Le Monde des
Utilisateurs de L’Analyse de Données)
• http://www.i-journals.org/ejs/index.php : website of the free online newspaper Electronic
Journal of Statistics
• http://siba-ese.unisalento.it/index.php/ejasa/index : website of the free online newspaper
Electronic Journal of Applied Statistical Analysis
• http://interstat.statjournals.net/ : website of the free online newspaper InterStat
• http://www.jds-online.com/ : website of the free online newspaper Journal of Data Science
• http://tbf.coe.wayne.edu/jmasm/ : website of the free online newspaper Journal of Modern
Applied Statistical Methods
• http://www.jstatsoft.org/ : website of the free online newspaper Journal of Statistical
Software
• http://www.i-journals.org/ss/index.php : website of the free online newspaper Statistics
Surveys
1.2.2
Data Analysis
Lecturers: Hervé de Milleville and Marietta Manolessou
1.2.2.1
Objective of the module
In Descriptive Statistics, a population is studied on one or two variables. Data analysis or multidimensional data analysis is an extension to several Variable-descriptive Statistics.
This course is a first approach to the different multidimensional analyses of methods used to
examine large masses of information. We shall discuss three types of problems: descriptive analysis,
explanatory models and classification. SAS software will be used on different data corpora.
At the end of the course, based on a corpus of multidimensional data, students will know how to:
• Identify which technique to use to solve a problem
• Prepare data sets to launch the associated technical program selected
• Interpret the results provided by the software
However, this remains an introduction course; to go further, students will need to:
• Look more closely at the technical data preparation (very little discussed in this module)
• Explore some of the methods discussed
• Discover new methods of analysis
The techniques introduced are illustrated during a series of tutorials via EXCEL.
1.2.2.2
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General principles of factor analysis
Analysis of Variance (Examples -Exercises)
Simple Linear Regression (Examples -Exercises)
Multiple Linear Regression (Examples –Exercises)
Correlation Analysis (Montgomery and Peck Theorem) (Examples –Exercises)
Non-Linear regression with transformed variables (Examples -Exercises)
Principal Components Analysis
Factorial correspondence analysis
1.2.2.3
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Topic in detail
Bibliography
Probability Statistics and Queuing theory with Computer Science Applications, Arnold O.
Allen, Academic Press, 1990
Analyse des données, Michel Volle, Economica
Probabilités Analyse des données et statistique, G.Saporta, Editions Technip
Factor Analysis as a Statistical Method, Lawley, D.N., Maxwell, A.E., Butterworths
Mathematical Texts, England, 1963
Multivariate Analysis, Mardia K.V., Kent J.T., Bibby J.M., Academic Press, London
1979
Printed tutorial by M. Manolessou
1.2.3
Introduction to Data Mining
Lecturer: Maria Malek
1.2.3.1
Objective of the module
This introductory course in data mining allows students to have a first approach of the problems
and applications of data mining. It also lets students learn several models and their application on
different types of data.
1.2.3.2
Topic in detail
1. Data Mining fields, Data Mining Process, Data Mining Tasks, Data and attribute natures.
2. Machine Learning: Supervised and unsupervised algorithms. Classification models, classifier
validation methodology. Precision and recall measures, confusion matrix, and cross validation
method.
3. Comparison of supervised and unsupervised models: K-nearest neighbours and K-means
algorithms
4. Supervised machine learning methods:
• Candidate elimination and version space
• Decision Trees: ID3 and C4.5 algorithms
• Neural Networks
5. Association Rules: Apriori and AprioriTid algorithms
• Association rules generation
• Properties of simple and strict redundancy
7. Meta Learning: Bagging and Adaboost
8. Comparative study and discussion
1.2.3.3
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Bibliography
Advances in Knowledge Discovery and Data Mining, Fayyad, G. Piatetsky-Shapiro, P. Smyth,
and R. Uthurusamy, AAAI/MIT Press, 1996
Data Mining: Practical machine learning tools and techniques, 2nd Edition, Ian H. Witten,
Eibe Frank, Morgan Kaufmann, 2005
1.2.4
Introduction to Prediction Models
Lecturer: Hervé de Milleville
1.2.4.1
Objective of the module
The purpose of this subject is the study of a sequence of numeric values representing the evolution of a
quantity over time (temporal or time series). Such value sequences can be expressed mathematically in order
to analyze behaviour, usually to understand the past and to predict future behaviour (short-term forecasting).
1.2.4.2
Topic in detail
The methods discussed are:
• Single and double moving averages
• Single and double Exponential Smoothing
• Holt-Winter Model
• The ARMA methods
• The detection of seasonality by autocorrelation
The software used is EXCEL and SAS.
1.2.4.3.
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Bibliography
Statistical Methods for Forecasting Bovas Abraham, Johannes Ledolter, Publisher: Wiley
Introduction aux séries temporelles, Master statistique et économétrie, Aragon Y.
Cours séries temporelles, DESS Mathématiques de la décision & DESS Actuariat, A. Charpentier
Méthode de prévision à court terme, Edition Ellipses, Mélard G.
Cours de séries temporelles, Maîtrise d’économétrie, Viano M.C.
Initiation à l’analyse des séries temporelles et à la prevision, Mélard G., Revue Modulad
2006, n°35 (free online press review)
1.3 Mathematics for computer science
1.3.1
Signals and Systems
Lecturer: Guy Almouzni
1.3.1.1
Objective of the module
The acquisition of basic knowledge in signal processing and systems theory.
1.3.1.2
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Time Representations of Signals
Time Representations of Systems
Frequency Representations of Signals
Frequency Representations of Systems
Sampling - Interpolation - Quantization
Linear filtering, Analysis & Synthesis of digital filters, Multirate filtering
1.3.1.3
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Topic in detail
Bibliography
Théorie et traitement des signaux, F. de Coulon, Dunod
Traitement du signal, P. Duvaut, Hermès
Traitement numérique des signaux, M. Kunt, Dunod
Méthodes et techniques de traitement du signal, J. Max, Masson
Applications of digital signal processing, A.V. Oppenheim, Prentice-Hall
Digital signal processing, A.V. Oppenheim, R.W. Schafer, Prentice-Hall
Signals and systems, A.V. Oppenheim, A.S. Willsky, Y.T. Young, Prentice-Hall
Signal analysis, Papoulis, McGraw-Hill
Automatique, M. Rivoire, J.L. Ferrier, Eyrolles
Signaux & systèmes linéaires, Y. Thomas, Masson
1.3.2
Signal Processing
Lecturer: Guy Almouzni
1.3.2.1
Objective of the module
The aim of this course is to allow the control and design of tools for signal processing applications in the field of
information processing.
1.3.2.2
Topic in detail
1. Mathcad simulation tool
Tutorial
2. Random signals, Autocovariance, Ergodicity
Transmitting a random signal in a linear system, Process for generating a random signal:
1st order formers filters, Generating process: MA, AR, ARMA
3. Signal synthesis, AR, MA, ARMA models, White noise
4. Characterization (Analysis - Frequency transforms)
• Cepstral analysis, Spectral Analysis, Wavelets, Correlation estimators
• DSP estimator: periodogram, correlogram, from the AR model of the signal
5. Signal conditioning, Denoising
Preaccentuation, Desaccentuation, Denoising
6. Transmission
Coding, Equalization, Adaptive filtering
7. Linear Prediction Coding
Linear Prediction Coding, Lossy compression
8. Optimal filtering
Least squares, RLS
9. & 10. Project
• Adaptive filtering
• Prediction (economical cycles)
• Optimal filtering
• Signal compression
• Conditioning – Filtering, Signal detection
• Signal characterization, Spectrogram, Wavelets
1.3.2.3
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Bibliography
Introduction à la théorie du signal et de l’information, F. Auger, Technip
Traitement numérique du signal : simulation sous Matlab, G. Blanchet, M. Charbit, Hermès
Traitement des signaux pour les systèmes sonar, M. Bouvet, Masson
Signal et communication numérique, J.M. Brossier, Hermès
Eléments de théorie du signal : aspects aléatoires, M. Charbit, Ellipses
Processus stochastisques : estimation et prédiction, M. Gevers, L. Vandendorpe, UCL
Traitement Numérique des Signaux, Atelier de TNS, M. Kunt, Dunod/PPR
Signaux & systèmes linéaires : cours/exercices, Y. Thomas, Masson
Traitement linéaire du signal numérique, F. Truchetet, Hermès
1.3.3
Simulation and Stochastic Process
Lecturer: Marietta Manolessou
1.3.3.1
Objective of the module
This course aims to study the properties of stochastic processes using simulation of random
variables. It is therefore strongly practice-oriented even if the main concepts and properties are
discussed in class.
1.3.3.2
Topic in detail
Simulation of probability laws
• “Random number generator
• Simulation experiments
• Simulation of laws
• Scheme polls to discrete distributions
• Inversion method for discrete distributions
• Inversion method for continuous distributions
• Simulation of the normal law by TCL
• Simulation of the normal law via Box-Muller
Stochastic Processes
• Definitions and properties
• Trajectories and states of a stochastic process
• Properties of a process
Markov Chain
• Definitions
• Transient, recurrent and absorbing states
• Convergence
White Noise
• Definition
• Simulation and validation
Brownian motion
• Definition
• Simulation: normalization method of random walk
• Simulation: Euler random method
• Validation (normality test)
Poisson process
1.3.3.3
Bibliography
• Markov models and algorithms, Bernard Ycart, Ed Springer-SMAI
1.3.4
Operational Research: Linear Optimization
Lecturer: Marietta Manolessou
1.3.4.1
Objective of the module
In this course, you will learn methods of linear optimization, and implement them.
1.3.4.2
Topic in detail
1. Linear Optimization (a) Simplexe -classical,
2. Penalties-Duality
3. Integer Numbers programming (Method of Decreasing Congruencies)
4-5. Dynamic Programming following Bellmann, Determinist cases, discrete and continuous cases
Non-deterministic discrete case
6-7. Transport and Affectation problems
1.3.4.3
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Bibliography
Linear programming and Extensions, G. Dantzig, N.J.Princeton, Princeton, University Press,
1963
Précis de Recherche Opérationnelle, R.Faure, Dunod, Paris, 1979
Linear Programming: Methods and Applications, 5th edition, S.Gass, New York, Mc Graw-Hill,
1985
Lectures of the Ecole Nationale Supérieure des Télécommunications, Paris
Combinatorial Optimization: Algorithms and Complexity, C. Papadimitriou and K. Steinglitz,
Englewood Cliffs, N.J. Prentice-Hall, 1982
1.3.5
Deterministic and Stochastic Optimization
Lecturer: Forest Jean Paul
1.3.5.1
Objective of the module
In this course, you learn nonlinear optimization methods and how to implement them on a
computer. Deterministic and stochastic methods and heuristic methods are addressed.
1.3.5.2
Topic in detail
Deterministic methods:
- Gradient
- Gradient with optimal steps
- Conjugate Gradient
•
- Newton's method
- Projection method
- Method with penalty
Methods with memory/ stochastic methods :
- Tau search
- Simulated annealing
- Genetic algorithms
1.3.5.3
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Bibliography
Combinatorial Optimization : Algorithms and Complexity, C. Papadimitriou, K. Steiglitz,
Englewood Cliffs, N.J. Prentice-Hall, 1982
Recent advances in Mathematical Programming, A. W. Tucker, Mc GRAW-HILL, New York
Operations Research : Applications and Algorithms, W.L. Winston, PWS-KENT, 1991
Optimisation Numérique, J.F. Bonnans, J.C. Gilbert, C. Lamarechal, C. Sagastizabal, Springer,
1998
Cours sur les méthodes d'optimisation, Littérature de physique et maths, A. Soukharev, A.
Timokhov, Moscow, 2008
Bases des méthodes d'optimisation, V. Lesine, U. Lisovetz, Mai, Moscow, 1998
Méthodes d'optimisation, V. Bonnaillie-Noel, ENS lectures of 2005-2006
Algorithmes de minimization, S. Chaznoz, A. Daare, Paris7 University lectures, CEA Saclay,
2005
Optimisation Quadratique, H. Zidani, P. Ciarlet, ENSTA lectures of 2005
Introduction dans les méthodes d'optimisation, A. Attenkov, V. Saroubine, Science, Moscow,
2008
Optimisation, I. Galeev, Science, Moscow, 2006
Numerical methods for least square problems, A. Bjorck, SIAM, 1996
Introduction à l'analyse numérique matricielle et à l'optimisation, P.G. Ciarlet, Masson, 1994
Convex Analysis and Minimization Algorithms, J-B. Hiriart-Urruty, C. Lemarechal, Springer,
1993
Linear and Nonlinear Programming, D.G. Luenberger, Addison-Wesley, 1984
Numerical Optimisation, J. Nocedal, S.J. Wright, Springer
1.3.6
Combinatorial Optimization and Complexity Theory
Lecturer: Jean-Paul Forest and Houcine Senoussi
1.3.6.1
Objective of the module
Introduce the theory of decidability through its themes (Can a problem be solved via a computer? classes of problems). Give students the means to assess the difficulty of a problem, what is feasible
(on computer) and what is not.
1.3.6.2
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Topic in detail
Algorithms and complexity
Graph theory
Turing machine
Formal languages
Decidability
Undecidable problems
Halting problem
 Complexity classes
 P and NP
 NP-complete problems
1.3.6.3
Bibliography
 Computational complexity, C. H. Papadimitriou Addison-Wesley, 1994
 Introduction à la calculabilité, 3rd edition, P. Wolper, Dunod, 2006
 Introduction to the Theory of Computation, 2nd edition, Michael Sipser, Course Technology,
2005
 Calculabilité et décidabilité, J.-M. Autebert, Masson
 Calculateurs, calculs, calculabilité, O. Ridoux, G. Lesventes, Eyrolles
1.3.7
Partial
Differences
Differential
Equations
and
Finite
Lecturer: Irina Kortchemski
1.3.7.1
Objective of the module
In this course we will study:
• Numerical and analytical methods to solve models commonly encountered in fluid mechanics,
telecommunications, biology, medicine, industry, finance, etc. All of these models are represented
by EDP.
• The different approaches to the discretization of PDEs, the stability and the convergence of
discrete equations. We will compare the analytical and numerical solutions in simple cases.
1.3.7.2
Topic in detail
Lecture 1. Mathematical modelling and differential equations in partial derivatives
Lecture 2. Ordinary differential equations
Lecture 3. Principles of finite difference method for the PEDs
• Mesh
• Taylor formula
• Discretization of derivatives
Lecture 4. Basic strategy in approaches to discretization
• Explicit Euler methods
• Implicit methods Crank -Nicolson
Lecture 5. Boundary conditions
• Dirichlet Boundary conditions
• Neumann Boundary conditions
• Periodic Boundary conditions
Lecture 6. Schemes on several different temporal levels
Lecture 7. Parabolic equations
• Thomas Algorithm
• Numerical solution of the heat equation by Crank-Nicolson, Implementation
Lecture 8. Consistency, Stability. Convergence, Lax theorem
Lecture 9. Elliptic Equations
• Discretization of boundary conditions
• Jacobi and Gauss-Seidel iterative methods, Sparse matrix
• Discretization and implementation in polar coordinates
Lecture 10. Hyperbolic equations -10.1 Advection equation
• Upwind scheme
• Lax-Friedrichs scheme
• Lax-Wendroff scheme
• Leap-Frog scheme
• Crank-Nicolson scheme
Lecture 11. Numerical solution of two dimensional heat equations
Lecture 12. Nonlinear PEDs
• Numerical solution of the one-dimensional Burgers equation
• Mac-Cormack method
• Crank-Nicolson method
• Numerical solution of the Korteweg de Vries equation
• Numerical solution of the Sine-Gordon equation
• Fourier analysis of PEDs, Dispersion relation
1.3.7.3
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Bibliography
Numerical Methods for Scientists and Engineers, H. M. Antia, Birkhauser
Numerical Modelling in Material Science and Engineering, M. Rappaz, M. Bellet, M. Deville,
Springer
Numerical Partial Differential Equations, J.W. Thomas
Numerical Methods for Partial Differential Equations, W.F. Ames, Nelson and Sons LTD.
London, 1969
Numerical solution of PDE : Finite difference methods, G.D. Smith, Clarendon Press, Oxford,
1978
Computational Methods for Fluid Dynamics, J.H. Ferziger and M. Peric, Springer, 1996
Numerical Recipes. The art of Scientific Computing, W. Press, S. Teokolsky, W. Vetterling,
Brian P. Flannery, Cambridge University Press, 2011
Computational Physics, N. Giorgano, H. Nakanishi, Pearson, Pearson Hall, 2009
1.4 Architecture and software
1.4.1
Object Oriented Modelling with UML
Lecturer: Bernard Glonneau
1.4.1.1
Objective of the module
This course aims to teach student modelling and design programs using the object approach. The
language used is UML. The purpose of this course is to:
• Provide a software development methodology to start off with in the real world until the
completion of the program
• Learn how to design objects that can later be re-used
1.4.1.2
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Topic in detail
Modelling: “Why”s and “How”s
What does UML contain, and what is left out?
Complex and “Knows relations, class diagram
Improving models with O.C.L
Who and which part of the software is involved, when and how? Use-case diagram
Who does what in what order? Scenarios
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What does ‘state’ mean for objects? State diagram
Object oriented design
Interface
Introduction to Design Patterns
1.4.1.3
Bibliography
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Conception et programmation orientée objet, Bertrand Meyer, Eyrolles
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Modélisation objet avec UML, Pierre-Alain Muller, Nathalie Gaertner, Eyrolles
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Object-Oriented Analysis and Design with Applications, 3rd edition, Grady Booch, Robert A.
Maksimchuk, Michael W. Engel, Bobbi J. Young, Ph.D. Jim Conallen, Kelli A. Houston,
Addison-Wesley, 2007
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UML specifications by the OMG (use the UML 2.0 version)
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OCL specifications by the OMG
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Designs Patterns - Elements of Reusable Object-Oriented Software, Erich Gamma, Richard
Helm, Ralph Johnson, John Vlissides, Addison Wesley
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http://laurent-audibert.developpez.com/Cours-UML/
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Object Management Group (OMG) : http://www.omg.org
1.4.2
Java
Object-oriented Design and Programming with
Lecturer: Rachid Chelouah
1.4.2.1
Objective of the module
This course involves learning object-oriented programming with the Java language and introducing
some design patterns.
1.4.2.2
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Programming paradigms
Classes and object members
UML Mapping of the association
Class members and inheritance
Packages
Interfaces - Eclipse
Collections
The exceptions
Input/output – Files and Flow
Generic programming
Enumerations - Annotations
1.4.2.3
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Topic in detail
Bibliography
Java in a Nutshell, David Flanagan, O'Reilly
La programmation objet en Java, Michel Divay, Dunod
http://java.sun.com/
JavaDoc JRE 1.6 and 1.7
http://www.oracle.com/technetwork/java/javase/downloads/index.html#docs
1.4.3
Relational Databases Modelling, Design and
administration
Lecturer: Nga Nguyen, Barth George
1.4.3.1
Objective of the module
Databases are now a central element of a vast majority of information systems, solving the issue of
long-term storage of complex and extensive data in a powerful and effective way.
Databases can also be regarded as an overlay file system, to provide an optimal and efficient way of
storing and especially accessing this data.
First, this course introduces the concept of databases and provides students with basic skills in
modelling, designing, handling and using data models.
Then we move on to the more advanced concepts:
• Optimal implementation of treatments on the DBMS
• Design of distributed databases
• Safety management through roles
• Optimization of queries on large data volumes
1.4.3.2
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Topic in detail
Basic concepts
● Entity-Relationship Model (conceptual data models, logical data models and
normalization) : 2 lectures
● SQL Data Definition Language : 1 lecture
● SQL Data Manipulation Language : 4 lectures
● Index and View : 1 lecture
● Transaction : 1 lecture
Advanced concepts
● Processing in a database
● PL/SQL language
● Triggers
● Procedures, functions and packages
● Distributed databases
● Concept : single MCD and multiple MLD
● Different kinds of fragmentation: horizontal, vertical and mixed
● Reconstituting views
● Materialized views
● Database links
● Roles in a database
● Application roles
● Other roles
● System privileges
● Object privileges
● Introduction to the administration of a database
● SPARC architecture
● Tablespace
● Repository
● Accelerators
● Indexes : b-trees, bitmaps, inversed
● Clusters
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1.4.3.3
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The request plan
Bibliography
Bases de données, G. Gardarin, Eyrolles, 2003
Base de données et Langage SQL, L. Audibert
Merise et UML pour la modélisation des systèmes d'information, J. Gabay, Dunod, 2001
Oracle PL/SQL Programming, Steven Feuerstein and Bill Pribyl, O'Reilly
The website with the best Oracle books: http://oracle.developpez.com/livres/
1.4.4
Architecture and Network Programming
Lecturer: Bernard Glonneau
1.4.4.1
Objective of the module
Students will discover and become familiar with the concepts and techniques of networks. The first
part leads to developments in Java, the second part to developments in C. they will also be
introduced to R.M.I. (Java) programming that is widely used in parallel computing.
1.4.4.2
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Network models
TCP Implementation in JAVA language
UDP Implementation in JAVA language
http Implementation in JAVA language
Proxy and firewalls
R.M.I. techniques in JAVA language
Network administration protocols
1.4.4.3
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Topic in detail
Bibliography
Réseaux: architecture, protocoles, applications, Andrew Tanenbaum, InterEditions, 1990
TCP/IP: architecture, protocoles, applications, Douglas Comer, InterEditions, 1992
Les Réseaux, Pujolle, Eyrolles
1.4.5
Parallel programming
Lecturer: Rachid Chelouah
1.4.5.1
Objective of the module
- Introduce general techniques and specific algorithms of parallel and distributed computing
- Discover new concepts related to cloud computing
1.4.5.2
Topic in detail
General concepts
• Multithreading programming with Java
• Multiprocessing programming with java
• Review the independence of loops
• Limiting threads
• Different modes of parallelization
• Taxonomy Flynn
• Complexity and Amdhal law
• OpenMp
• MPI
1.4.5.3 Bibliography
•
OpenMP official website: http://www.openmp.org
Project management
1.4.6
V-Model and Agile Methods
Lecturers: Bernard Glonneau and Rachid Chelouah
1.4.6.1
Objective of the module
The objective of the course is to explain the two main methods of project management used today in
software development projects: the V-model and Agile methodologies.
1.4.6.2
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From V-model to Agile methods
The manifesto and the panorama of Agile methods
SCRUM
XP
Kanban
1.4.6.3
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Topic in detail
Bibliography
Extreme programming pocket guide, author: Chromatic, editor: O'Reilly
Les services agiles et les processus, authors: Thierry Chamfrault and Claude Durand, editor:
Dunod
Balancing Agility and Discipline: a Guide for the Perplexed, authors: Barry Boehm et Richard
Turner, editor: Addison Wesley
Scrum : le guide pratique de la méthode agile la plus populaire, author: Claude Aubry, editor:
Dunod
1.4.7
Initiation to the research
Lecturer: Rachid Chelouah
1.4.7.1
Objective of the module
The objective of this module is to introduce students to methodologies for scientific analysis of
documents related to one of the three pillars of the master. This allows the student to prepare for
the final research project.
1.4.7.2
Topic in detail
Students will be provided with scientific papers relating to their courses of the current semester
(Parallel Architecture, Data mining, Networking, etc.), and they will need to:
● Choose one paper and make a scientific comment of it as if they were a reviewer
● Present the author’s problems
● How the authors have modelled theirs problems
● The methods chosen by the authors to solve their problems
● How the authors interpreted their results
● Comment on whether they have presented different perspectives to their work
● Analyze whether the bibliography is recent enough, well adapted to their studies, etc.
1.4.8
Transverse Project and finalized research
1.4.8.1
Objective of the module
The objective of this module is to confront the students with a large project in the working
conditions of a professional environment: the development of a decision-support software around
the issues of Big Data.
At the end of this project, students will have a real experience of an IT project in:
● Project management
● Unit testing and integration testing
1.4.8.2
Topic in detail
Students will be required to follow these steps:
• Conduct interviews
• Detailed Functional Specifications
• Definition of the different teams and resource management projects (svn, etc.)
• Detailed Specifications
• Modelling and design of the database
• Design and development of unit modules
• Integration of modules in a development environment or pre-production
• Recipe and beginning of production
1.5 FLE Beginners
1.5.1 FLE
Lecturer: Lamouri Ines
1.5.1.1. Objective of the module
Students will reach a sufficient level for good communication in everyday academic and professional
life.
1.5.1.2. Topic in detail
• Everyday French
• Discovering the key aspects of French culture to facilitate integration
• Working from texts and audio-visual materials
• Using the language from personal experiences in the field
• Mastering the general vocabulary that is useful to life in school
• Systematic vocabulary acquisition using methods that reflect the four skills
The year will be punctuated with level tests to monitor the smooth progress of students.