Data Engineering and Artificial Intelligence
Data Analytics | Data Science | Big Data | Artificial Intelligence | Software
Benjamin, Directeur de Projet - ADEO Services
The department of Data Engineering and Artificial Intelligence trains multiskilled engineers in the processing of information, both in its statistical and computational forms, for use in various business professions. Given the cross-disciplinary nature of IT and statistics as disciplines, the areas of activity in which they appear are numerous, especially in the tertiary sector and the IT aspects of the secondary sector.
Apart from the teaching of scientific concepts and the mastery of the tools used, the courses taught place particular importance on the learning of methods (ie the qualities of rigor, curiosity and inventiveness in scientific approaches) and the development of the personality of the student (communication and listening skills, team work, autonomy…)
During the 3rd year (semesters 5 and 6), courses focus mainly on the teaching of fundamental subjects, based on the three main areas of specialization of the department – IT, statistics and humanities. The specific classes offered to student engineers depend on the particular areas of previous study.
During semesters 7 and 8, the student engineer deepens his/her knowledge of statistics (statistical modelling, exploratory statistics…), IT (object-oriented programming, architectural software, data bases, advanced systems….) and project management (information system projects, project management…). During this year, greater emphasis is placed on projects carried out in groups.
During the 9th and 10th semesters, the student chooses a specialization through the choice of optional courses. These include software engineering, data mining, statistical methods for marketing, the mathematics of new financial products, bio-statistics…The specialization is also determined through the choice of final year project and placement.
Classes are taught in French.
Activity sectors
- SSII (Sociétés de Services en Ingénierie Informatique – computer engineering and maintenance companies)
- Banking and Insurance
- Major retail outlets
- Public administration
- Research
Links with research units
Associated research unit :
- CRIStAL - (computer science, signal and automatic control : covers many digital science thems, from the most theoretical to the most applied... artificial intelligence, cybersecurity, digital health, robotics)
- Laboratoire Paul Painlevé - (probability and statistics, associated with the CNRS)
- -
In most cases, the lecturers in this speciality carry out their research activities within these laboratories. The student engineers can thus undertake projects or work placements in areas related to the specialities of these research laboratories.
Program
Program
UE 5-1 - Fundamental of Mathematics
90h
ECTS
Hours
111100
UE 5-1 - Fundamental of Mathematics
7
90 h
111110
Mathematical Support 1
- Goals to be achieved :
* Review the fundamental concepts of mathematics (part 1) - Course details :
- Reasoning and logic
- Limits, continuity, derivation, integration (on a segment)
- Exponential and logarithm functions
- Study of functions
- Trigonometry and complex numbers
- Numerical sequencies - Reading list :
16 h Tutorial
111120
Mathematical Support 2
- Prerequisites :
111110
- Goals to be achieved :
* Review the fundamental concepts of mathematics (part 2) - Course details :
Analysis
- Multiple integrals (bounded region)
- Polar coordinates
- Series expansions
Algebra
- Determinant of a matrix
- Inversion of matrices
- Solving systems of linear equations - Reading list :
16 h Tutorial
111130
Mathematical Basics
3
40 h
- Prerequisites :
111110|111120
- Goals to be achieved :
* Review the fundamental concepts of mathematics (part 3) - Course details :
Analysis
- Improper integrals
- Numerical series and power series
- Sequences and series of functions
Algebra
- Normed vector spaces
- Scalar product, Euclidean norm, orthogonality
- Gram-Schmidt orthonormalisation
- Orthogonal projection - Reading list :
12 h Lecture
28 h Tutorial
3 h DS
28 h Tutorial
3 h DS
111140
Probability 1
2
20 h
- Goals to be achieved :
* Be familiar with tools for counting (arrangements, permutations and combinations) of finite sets, especially for computing probabilities in the equiprobable case.
* Understand the notion of a probability space and its elementary properties. Be able to use the vocabulary of the set theory to describe events.
* Understand the notion of independence of events and be able to calculate (conditional) probabilities with additional information - Course details :
1. Enumeration
1.1 Vocabulary for sets
1.2 Finite sets and enumeration
1.3 Countable sets
2. Probability
2.1 Notion of random experiment
2.2 Events
2.3 Probability as a function of sets
3. Conditioning and independence
3.1 Conditioning
3.2 Independence of events - Reading list :
10 h Lecture
10 h Tutorial
1 h DS
10 h Tutorial
1 h DS
111150
Probability 2
2
30 h
- Prerequisites :
111130|111140
- Goals to be achieved :
* Understand the concepts of real random variable and probability law
* Be able to describe and interpret the distribution function of a variable
* Be able to describe and interpret the laws of discrete variables by the mass function and that of continuous variables by the density
* Know the usual laws
* Be able to calculate the expectation and moments of a variable
* Understand the notion of characteristic function of a variable - Course details :
1. Definition of a random variable (we will consider the notion of sigma algebra and probability).
2. Types of random variables: quantitative, qualitative.
3. Discrete and continuous real random variables.
4. Probability distribution of a discrete random variable. Discrete random variable models (Uniform, Binomial, Poisson, Geometric).
5. Probability distribution of a continuous random variable (density). Continuous random variable models (Uniform, Exponential, Normal).
6. Distribution function. Properties (quantiles, link with density, transformation of a v.a.).
7. Characterization of an a.v.: moments of a random variable.
8. Properties of expectation and variance. Markov inequality. Illustration of the main models.
9. Characteristic function - Reading list :
14 h Lecture
12 h Tutorial
4 h Practical
1 h DS
12 h Tutorial
4 h Practical
1 h DS
UE 5-2 Fondamental of Computer Science
114h
UE 5-3 Databases
64h
UE 5-4 Languages and Soft skills
120h
UE 6-1 Statistics and Decision Aid
230h
ECTS
Hours
112100
UE 6-1 Statistics and Decision Aid
15
230 h
112110
Probability 3
2
34 h
- Prerequisites :
111150
- Goals to be achieved :
* Have a unified view of discrete and continuous random variable through basic notions of measure theory
* Understand the notion of random vector and the related notions
* Understand the usual convergences of random variables
* Be able to use the classical theorems LGN and TCL
* Be able to calculate conditional distributions, expectations and variances - Course details :
- Chapter 1: Theory of measure in a nutshell
- Chapter 2: Random vectors
- Chapter 3: Independence of real random variables
- Chapter 4: Moment generating function
- Chapter 5: Convergence of sequences of real random variables and limit theorems
- Chapter 6: Distributions and conditional expectations - Reading list :
14 h Lecture
14 h Tutorial
6 h Practical
1 h DS
14 h Tutorial
6 h Practical
1 h DS
112120
Statistical Inference
4
58 h
- Prerequisites :
112110
- Goals to be achieved :
* Understand the main concepts of inferential statistics: sampling, estimation, statistical testing
* Be able to use the first decision support tools : estimators, parametric and non-parametric tests
* Be able to implement applications in R - Course details :
Sampling, descriptive statistics
Estimation
Minimum-variance unbiased estimation
Method of moments, maximum likelihood and Bayesian estimators
Confidence intervals
Statistical hypothesis testing
Tests for a single population : tests on parametres, goodness-of-fit, independance
Tests for comparison of two populations
Analysis of variance - Reading list :
G. Saporta, "Probabilités, analyse des données et statistique", Editions Technip, 1990
A.A. Borovkov, "Statistique mathématique", Editions Mir, 1987
28 h Lecture
18 h Tutorial
12 h Practical
1 h DS
18 h Tutorial
12 h Practical
1 h DS
112130
Linear regression
1
20 h
- Prerequisites :
112120
- Goals to be achieved :
* Be able to estimate and interpret a linear regression model
* Be able to choose variables and set up a linear model
* Be able to implement linear models in R - Course details :
- The simple linear regression
- The least squares criterion
- The Gaussian case and the Gauss- Markov theorem
- Multiple linear regression
- Quality of a linear model
- Selection of variables in linear regression
- Applications in R - Reading list :
Gilbert Saporta, Probabilités, analyse des donnees et statistiques, Ed. Technip
10 h Lecture
10 h Practical
1 h DS
10 h Practical
1 h DS
112210
Numerical linear algebra
3
42 h
- Prerequisites :
111130|111210|111230
- Goals to be achieved :
* Understand the main numerical linear algebra methods (direct methods, iterative methods, system solving, eigenvalues)
* Be familiar with some examples of problems to which they apply (principal component analysis)
* Understand the notions of stability and conditioning
* Be able to use some corresponding Python libraries - Course details :
Linear system solving : forward and backward substitutions, LU factorizations (Gauss, Cholesky), QR factorization (Householder)
Eigenvalues : the power method, the inverse power method, the QR algorithm, the bisection method
Singular value decomposition
Application to principal component analysis - Reading list :
1. Introduction à l'analyse numérique matricielle et à l'optimisation, Ph. Ciarlet, Masson,
2. Valeurs propres de matrices, F. Chatelin, Masson,
3. Analyse numérique matricielle appliquée à l'art de l'ingénieur. P. Lascaux et R. Théodor, Tome 1 et 2, MASSON,
4. Matrix Computation, G. Golub et Ch. Van Loan, John Hopkins University Press.
5. Numerical Linear Algebra, L. Trefethen, SIAM
20 h Lecture
10 h Tutorial
12 h Practical
1 h DS
10 h Tutorial
12 h Practical
1 h DS
112220
Graph theory and Combinatorics
2
30 h
- Goals to be achieved :
* Know how to model a problem using graphs
* Understand the specific data structures for graphs
* Understand the different types of graph
* Master main graphs algorithms - Course details :
- Introduction to graphs
- Data structures for graphs
- Trees
- Shortest path problem
- Simple scheduling
- Maximal flow problem
- Reading list :
- Algorithmes de graphes, Ed. Eyrolles, Lacomme-Prins-Sevaux
- Optimisation combinatoire (vol 2 : programmation discrète), Ed. Hermann, Sakarovitch
16 h Lecture
14 h Tutorial
1 h DS
14 h Tutorial
1 h DS
112230
Project Graphs for Optimization
1
14 h
- Prerequisites :
112220
- Goals to be achieved :
* Be able to model an optimization problem using graphs
* Be able to implement an algorithmic solution in C
14 h Project
112240
Data Mining
1
22 h
- Prerequisites :
111260|111210
- Goals to be achieved :
* Understand tasks and techniques of data mining
* Understand the main tasks of machine learning: non supervised learning (clustering), supervised learning (classification), association rules - Course details :
- Introduction to data mining and machine learning
- Non supervised learning (Clustering): partitioning clustering, hierarchical clustering, density based clustering.
- Supervised learning (Classification): K-NN, decision trees, Bayesian classification, neural networks
- association rules: A-priori
- Ensemble methods : bagging, boosting, random forest, Adaboost, ...
- Data preparation: missing data, discretization, feature selection
12 h Lecture
10 h Practical
1 h DS
10 h Practical
1 h DS
112250
Project Data WEB
1
10 h
- Prerequisites :
111230|112360|111220
- Goals to be achieved :
* Be able to scrap data from the Internet, clean and exploit it
* Be able to visualize descriptive statistics using web applications - Course details :
Project to be carried out in pairs
The aim is to build a WEB page to display relevant information on the data recovered OpenData in connection with the "DDRS"
10 h Project
UE 6-2 Computer Science
158h
UE 6-3 Languages and Soft skills
75h
UE 7-1 Statistical Modelling and Decision Support
179h
ECTS
Hours
113100
UE 7-1 Statistical Modelling and Decision Support
14
179 h
113110
Exploratory statistics
3
38 h
- Prerequisites :
112120
- Goals to be achieved :
* Be able to perform multivariate statistical analysis
* Be able to interpret the results of a multivariate analysis
* Be aware of the need for multivariate statistics in high dimension
* Be able to implement applications in R and SPAD - Course details :
1 Principal component analysis
2 factorial correspondence analysis
3 multiple corresponding Analysis
4 classification method ( partitioning, hierarchical clustering )
5 Linear Discriminant Analysis - Reading list :
Gilbert Saporta: Probabilités, analyse des données et statistique (ed. Technip)
20 h Lecture
18 h Practical
1 h DS
18 h Practical
1 h DS
113120
Supervised classification
2
20 h
- Prerequisites :
112130
- Goals to be achieved :
* Be able to build and evaluate a supervised classification statistical model
* Be able to implement applications in R - Course details :
1-Introduction + Reminders on classical tests (Chi2, Cramer, MANOVA)
2-Factorial discriminant analysis
3-Probabilistic discriminant analysis (homo and heteroscedastic)
4-Rule evaluation (Bayes rule, ROC curve)
5-Logistic regression
(6-Random forest) - Reading list :
Gilbert Saporta, Probabilités, analyse de données et statistique (ed. Technip)
8 h Lecture
2 h Tutorial
10 h Practical
1 h DS
2 h Tutorial
10 h Practical
1 h DS
113130
Markovian random models
3
42 h
- Prerequisites :
112110
- Goals to be achieved :
* Be able to model random Markovian dynamical systems in discrete and continuous time
* Be able to predict their long time behavior
* Be able to implement applications - Course details :
Markov chains: definition, examples and properties
Classification of states: irreducible classes, recurrence and transience, periodicity
Stationary measures and limit theorems
Properties of the exponential law
Poisson process: definition and properties
Markov jump processes: definition, transition rate and generator, stationary measures and limit theorems
Queuing systems: Kendall's notation, M/M/1 queue example
Project study of the long-time behavior of Markovian systems, with highlighting of this behavior by numerical simulations and statistical studies - Reading list :
B. Ycart, "Modèles et algorithmes markoviens", Springer, 2002
22 h Lecture
20 h Tutorial
3 h DS
20 h Tutorial
3 h DS
113140
Project Markovian Random Models
1
15 h
- Prerequisites :
113130
- Goals to be achieved :
- Know how to develop a solution based on Markov random models
- Know how to implement this solution in a programming language
15 h Project
113150
Numerical computing for optimisation
2
24 h
- Prerequisites :
112210
- Goals to be achieved :
* Understand the mathematics and numerical computing notions dedicated to optimization
* Be able to implement applications in Python - Course details :
Linear least squares (normal equations vs QR factorization)
Newton method in one variable (solving an equation, minimizing an objective)
Functions of many different variables (graph, Taylor expansion, gradient vector, Jacobian matrix)
System solving (Newton scheme)
Unconstrained optimization (Hessian matrix, Newton scheme, gradient descent, stepsize control, local minima, maxima and saddle points)
Constrained optimization (Lagrange multipliers)
Automatic differentiation
Practical work in Python, autograd library, application to parameter estimation - Reading list :
* Initiation à l'analyse numérique, R. Théodor, Masson.
* Introduction à l'analyse numérique des equations aux derives partielles, P. Raviart et J. Thomas, Masson
* Methods of Numerical Integration, Davis-Rabinowitz, Academic Press
12 h Lecture
12 h Practical
1 h DS
12 h Practical
1 h DS
113160
Operational research
2
28 h
- Prerequisites :
112220
- Goals to be achieved :
* Presentation of several operations research methods
* Be able to model problems using linear programming
* Have notions of complexity when solving problems
- Course details :
- Introduction
- Linear programming
- Duality
- Transportation problem
- Integer programming
- Dynamic programming
- Goal programming
- Reading list :
H. A. Taha, «Operations research : an introduction», Prentice Hall, ISBN 0 13 272915 6, 1997.
18 h Lecture
10 h Tutorial
1 h DS
10 h Tutorial
1 h DS
113170
Project Operationnal research
1
12 h
- Prerequisites :
113160
- Goals to be achieved :
- Modeling and solving complex optimisation problems using Lingo/Cplex
- Analysis and application of results
- Understanding duality through sensitivity analysis - Course details :
- Lab 1: Modeling and problem-solving using Lindo
- Lab 2: Sensitivity analysis using Lindo
- Project: Analysis of a business situation, optimization, and decision support
12 h Project
UE 7-2 Software Engineering and Information Systems
96h
UE 7-3 Awareness
20h
UE 7-4 Languages and Soft skills
102h
UE (S7)_GP
24h
UE 8-1 Statistics and Decision Making
108h
ECTS
Hours
114100
UE 8-1 Statistics and Decision Making
9
108 h
114110
Advanced Statistical Modelling
2
24 h
- Prerequisites :
113110|113120
- Goals to be achieved :
* Understand the penalized least squares methods for linear regression
* Understand the contribution of non-parametric modeling in regression and classification
* Be able to use random and fixed effects in an analysis of variance model
* Be able to implement applications in R and Python - Course details :
Ridge regression
The lasso regression
Regression quantile
Additive models
Analysis of variance repeated measures
R and SAS applications
12 h Lecture
12 h Practical
1 h DS
12 h Practical
1 h DS
114120
Project of Advanced Statistical Modelling
1
16 h
- Prerequisites :
114110
16 h Project
114130
Time Series
2
24 h
- Prerequisites :
112120|113110|113130
- Goals to be achieved :
* Be able to model and analyze a time series
* Be able to predict its behavior
* Be able to implement applications - Course details :
Description of a time series
Exponential smoothing methods
Trends and seasonality
ARMA stationary series modeling
Non-stationary series modeling SARIMA
Introduction to ARCH - GARCH processes
10 h Lecture
14 h Practical
1 h DS
14 h Practical
1 h DS
114140
Machine learning
2
22 h
- Prerequisites :
113110|113120|113150
- Goals to be achieved :
* Know the fundamental principles of machine learning
* Be able to model machine learning problems
* Be able to adopt a rigorous and scientific approach
* Be able to use a library for machine learning - Course details :
Lecture part :
- Review the overall data processing chain
- See or review different supervised learning methods
- Understand the role of hyperparameters and understand automation
- Take a look at neural networks and deep learning
Practical part:
- Familiarise yourself with the Scikit-Learn library (in Python)
- Use supervised classification methods: decision trees and set methods
- Compare different experimental protocols
- Learn about classical hyperparameterisation methods
- Approach neural networks and deep learning using the NNI platform - Reading list :
10 h Lecture
12 h Practical
1 h DS
12 h Practical
1 h DS
114150
Project of Machine Learning
1
10 h
- Prerequisites :
114140
- Goals to be achieved :
* Be able to analyze large datasets
* Know how to model supervised classification solutions
* Be able to implement optimized, high-performance methods
10 h Project
114160
Data Privacy
1
12 h
- Goals to be achieved :
* Know the principles and actors of the data
* Know the rights of individuals
* Know the regulations on Internet tracers (such as cookies) and profiling - Course details :
Outline :
1. Introduction to Data (the aim is to introduce students to the world of Data, with a focus on personal data law and cybersecurity: an opportunity to explain the history of this protection and the Data challenges).
2. Concepts and players (the aim is to define data protection terms - personal data / sensitive data / main principles of the RGPD)
3. People's rights (the aim is to make students understand that everyone has rights over their data: it is therefore important to protect data as a Data project manager, for example, but also as a "person", knowing that the latter will also be able to obtain their data if they request it).
4. Cookies and other tracers (the aim is to explain the regulations surrounding this subject, regulations which are changing a lot and which enormously concern future Data Scientists, Data Project Managers, etc. It's all about tracking web surfers on web sites. It is essential to program your cookies and trackers correctly, to avoid risking fines for your company).
5. Profiling (As with cookies/trackers, profiling is an essential subject, subject to numerous sanctions in the event of non-compliance with consent when the latter is necessary, for example, or to justify a legitimate interest. It is essential to define profiling in accordance with European regulations and its impact on a future data project manager (e.g. in a company).
"Quizzes" in Kahoot format are created to make the course dynamic and enable better learning.
The exam is in MCQ format: 40 questions.
12 h Lecture
1 h DS
1 h DS
UE 8-2 - Software Systems and Databases
102h
UE 8-3 Languages and Soft skills
63h
Unit Internship 8-4
120h
UE 9-1 - Data sciences
62h
ECTS
Hours
115100
UE 9-1 - Data sciences
4
62 h
115110
Deep Learning
2
29 h
- Prerequisites :
112240|114140|113150
- Goals to be achieved :
* Be able to explain the main components involved in training a multi-layer perceptron (MLP)
* Know the main deep learning models (MLP, CNN, RNN, Transformers), and being able to briefly justify their uses and application domains
* Be able to apply this knowledge by using the most important functions in PyTorch - Course details :
* Neural Networks and Deep Learning
* Optimization algorithms : GD - SGD - momentum - RMSProp - Adam
* Backpropagation principles
* Regularization
* Convolutional Neural Networks - Vision applications
* Recurrent Neural Networks - Language applications
* Attention and Transformer Networks - Language applications
* Autoencoders - Reading list :
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning
* François Fleuret, The Little Book of Deep Learning
14 h Lecture
14 h Practical
1 h DS
14 h Practical
1 h DS
115120
Special Databases
1
19 h
- Prerequisites :
113250|114240|114230
- Goals to be achieved :
- Identify the main types of modern databases (graph, vector, temporal, multi-model, NewSQL) and their characteristics.
- Understand the trade-offs (consistency, performance, scalability, expressiveness) associated with different types of databases.
- Analyze an application problem to determine requirements in terms of storage, querying, and performance.
- Select an appropriate database system, justifying this choice in light of technical constraints and use cases.
- Formulate and understand queries tailored to different data models, identifying the specific features of the associated query languages and paradigms. - Course details :
This module provides an overview of modern database architectures beyond traditional NoSQL models.
After reviewing the role of non-relational databases, the course explores various specialized models such as graph, vector, temporal, multi-model, and NewSQL databases.
For each type, the operating principles, typical use cases, and advantages/limitations are examined.
The course emphasizes the ability to select a database suited to a given need, taking into account constraints related to performance, consistency, and scalability. - Reading list :
6 h Lecture
12 h Practical
12 h Practical
115130
Cryptology
1
17 h
10 h Lecture
6 h Practical
1 h DS
6 h Practical
1 h DS
UE 9-2 Business use cases
116h
UE 9-3 - Accounting, Management, Communication 3
101h
UE 9-4 - Final year Project
90h
UE 9-4 Formative assessment
8h
UE 9.1 Inspire Decarbonized Energy
102h
UE 9.2 Inspire Resource Optimization
96h
UE 9.3 Inspire Climate Change
82h
UE 9.4 Inspire Acceptability and Leadership
120h
UE 9.4 Inspire_Alternance_ Work-Based Learning Activiti
36h
10-1 Unit Engineer placement
400h
ECTS
Hours
926100
10-1 Unit Engineer placement
30
400 h
926110
Engineer Placement
30
400 h
400 h Project
UE 10-1 Formative assessment
16h