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.

Contact the specialty

+33 (0)3 28 76 73 10

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.

UE 5-1 - Fundamental of Mathematics

90h

ECTS

Hours

111100

UE 5-1 - Fundamental of Mathematics

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

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

111140

Probability 1

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

111150

Probability 2

30 h

Prerequisites :
111140|111130|111250
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

UE 5-2 Fondamental of Computer Science

114h

ECTS

Hours

111200

UE 5-2 Fondamental of Computer Science

114 h

111210

Algorithmics and procedural programming

5.5

64 h

Goals to be achieved :
* Be able to analyse elementary programming problems.
* Know the control structures, the notion of sub-programmes, basic types, arrays and structures.
* Be able to design an algorithmic solution for these problems
* Be able to translate these solutions into the C language
Course details :
1. System, network: user aspects (C and TP)
System: classification systems, introduction to UNIX, introduction to computer file systems, shell (main commands), I/O redirections, pipes.
Network: network categories (local, internet), main internet services.

2. Algorithmics and data structures (C and TD)
Analysis and top-down conception: parameterized actions and functions
Sequences and recursivity, space and time complexities
Elementary data structures and associated algorithms (matrices and struct data type)
Elements of sorting

3. Programming in C (C and TP)
Introduction to C programming
Steps in production an executable
Functions and argument passing
Arrays, struct data type
Reading list :

14 h Lecture
22 h Tutorial
20 h Practical
8 h Project
1 h DS

111220

Numerical programming

1.5

22 h

Prerequisites :
None
Goals to be achieved :
* Know the coding of a floating point number
* Know the principle of some classical numerical algorithms for functions of one real variable (evaluation, polynomial interpolation, cubic splines, computation of definite integrals) as well as some concepts related to them (specificities of floating point computing, notions of relative and absolute errors, notion of order)
* Be able to write basic numerical computation programs in Python using numerical computation libraries such as numpy and matplotlib
* Be able to write tests for these programs
Course details :
Numerical computing: introduction to numerical computing specificities (formulas which are equivalent algebraically but non equivalent numerically) ; interpolation (Neville algorithm, Newton divided differences ; cubic splines) ; numerical integration (Newton-Cotes formulas, Gauss quadratures)

Programming: Python; vectors and matrices with numpy; graphism with matplotlib; test writing
Reading list :

6 h Lecture
16 h Practical

111230

Support in Algorithmics and Programming

Goals to be achieved :
Tutoring module for the course Algorithms and Structured Programming (S5)
Course details :
Depending on the assessment of the many continuous tests carried out in "Algorithms and Structured Programming", students are required to attend certain sessions.
Reading list :

24 h Tutorial

111240

Hardware

16 h

Prerequisites :
111210|111220
Goals to be achieved :
* Understand how a computer works and its hardware architecture.
* Be able to manipulate the binary coding of numbers and other numerical data
* Know the basics of Boolean algebra and electronic components
* Know current hardware architecture: the processor, memories, input/output, buses and caches
* Understand the technical documents of a PC
Course details :
Outline:
- History and definition of computers
- Binary and signed integer coding
- Information encoding
- Boolean algebra and circuits
- Computer architecture
- Micro-programming and processor operation
Reading list :

16 h Lecture
1 h DS

111250

Introduction to software for statistics

12 h

Goals to be achieved :
* Know how to use R and Python software for mainly statistical purposes: write and run a program.
* know the semantics and manipulate common objects
* Be able to study a set or table of data using simple descriptive statistics.
* be able to model a random phenomenon and perform one or more simple statistical tests
* Know open-source and commercial statistical software.
Course details :
1-Know the semantics and manipulate the usual objects in R and Python: qualitative and quantitative variables; write simple and advanced functions; vectors; vectorization; matrices; dataframes; tuples; lists; dictionaries.
2-Study data: export/import data sets or tables; dplyr package (R) and pandas module (Python); simple and complex graphing (ggplot2, addins, matplotlib,...); basic statistical indicators (mean, median, corrected and uncorrected variance, standard deviation, quantiles...); linear regression; simple statistical tests (Student...).
3-Model a random phenomenon: know the syntax of probability laws in R and Python (density, distribution function, quantiles, generate random values...); emulate a probability law and compare with predefined functions in R and Python; quantile-quantile diagram.
4-Acquaintance with free and proprietary statistical software: presentation of proprietary software SAS and SPSS; study of related lines of code (procedures, data, macro...); comparison with free software R and Python.
Reading list :

8 h Practical

UE 5-3 Databases

64h

ECTS

Hours

111300

UE 5-3 Databases

64 h

111310

Databases and Information System modelling

3.5

42 h

Goals to be achieved :
* Know the concepts and implementation of relational database management systems
* Know how to design (UML modelling) and build relational databases
* Know the relational algebra concepts
* Ability to formulate SQL queries
* Be able to manage users and their rights over the various objects that make up an SQL database
* Be able to develop an initial software application with access to databases
Course details :
* Architecture of a database system, Model
* Physical
* Relational model : définitions, dependancy and normalizing, integrity constraint.
* Conceptual model and computer science analysis (introduction UML)
* Development of a database
* Language of definition and query of data
Reading list :

8 h Lecture
8 h Tutorial
18 h Practical
8 h Project
1 h DS

111320

Project Databases and WEB

1.5

22 h

Prerequisites :
111310
Goals to be achieved :
* Be able to write a set of specifications based on a set of requirements for a small application.
* Be able to propose a suitable modeling solution in the form of a conceptual diagram.
* Be able to implement this solution using a relational database.
* Be able to develop a simple web site to manage and access the database.
Course details :
The project is carried out in twos.
The aim of the project is to build a database interfaced with a website, in line with the proposed specifications.

The project is divided into 2 phases, with the tutor playing a different role. During the first 6 hours of sessions, the tutor takes on the role of a project donor, and is therefore unaware of the databases involved. An analysis report is requested, and feedback is given to each pair before the 2nd phase begins. During the remaining 16 hours, the tutor now plays the role of expert, and is therefore able to help the students in their choice and execution of the project.

At the end of the project, each student is asked to assess himself/herself on the following points: consideration of specifications / project modeling / implementation, code quality, file organization / site functionalities and ergonomics / application user guide / report writing.
The final score is calculated from 10 and a weighting of the previous elements.
A bonus/malus is awarded for good/bad self-assessment.
Reading list :

22 h Project

UE 5-4 Economics, Soft Skills

72h

ECTS

Hours

111400

UE 5-4 Economics, Soft Skills

72 h

921110

Ecological and Societal Transition Challenge

34 h

Goals to be achieved :
The objective of the PCIS module is to introduce learners to work in project mode in inter-specialties. It aims to develop the following skills
- Ability to transpose an idea into precise and well-argued technical elements
- Ability to make a demonstrator and present it
- Ability to build a value chain
- Ability to translate a need into a service/product offer
- Ability to formulate a structured message with the aim of gaining support
- Ability to speak or present the project convincingly
- Ability to know and self-assess
- Ability to manage skills and make professional choices
Course details :
The CTES is carried out in inter-specialty groups of 7-8 students, around the development of an innovative product or service, from the idea to the proof of concept and the business model. In this context, students have 5 working sessions of 2 hours in groups on: creativity, concept writing, monitoring and economics, study of use and comparison and proof of concept. Then they have 2 days to produce the deliverables. Themes linked to sustainable development and social responsibility based on ADEME “2050 transitions” scenarios are imposed.
Learners have to :
- write the presentation of the concept and distribute the roles within the group (fortnight of the start of the school year),
- carry out the proof of concept and the drafting of the file (over 2 days)
- support orally, in front of a jury, their productions.
Throughout the course, learners will be made aware of the competency-based approach.

Large group project mode, hybrid teaching
Multidisciplinary approach, initiation to project mode, adaptable according to the number of learners, development of softskills (collaboration, problem solving, creativity, etc.)
Reading list :

3 h Lecture
16 h Tutorial
3 h Practical
12 h Project

921120

Economics

18 h

Goals to be achieved :
Understand economic constraints and be able to integrate them into
business strategies and professional strategies.
Integrate the international and multidisciplinary dimension of the questions
economic.
Understand and integrate the constraints of sustainable development: environment,
economy, social and governance.
Course details :
- INTRODUCTION TO ECONOMY
What is the economy?
What do we produce? How ?
What are the major economic aggregates?
+ indicator limit

- CORPORATE SOCIAL AND ENVIRONMENTAL RESPONSIBILITIES

– CURRENCY AND FINANCING OF THE ECONOMY
public debt

- DIGITAL ECONOMY

- INTERNATIONALIZATION OF COMPANIES
Reading list :

921125

Fundamentals of Management

10 h

Goals to be achieved :
Goals
The course aims to enable students to know:
- management in the sense of human resources
- the different entry points for understanding management in a company
- the visions of management developed by theoreticians (and some practitioners) since the beginning of the 20th century
- the different forms of leadership.

Skills acquired at the end of the course
At the end of this course, students will have developed skills in:
- to integrate into an organization, to animate it and to make it evolve
- take into account professional issues
- implement the principles of sustainable development
- take into account and enforce societal values
Course details :
Definition of management and positioning in relation to other concepts such as management, organization, strategy, etc.

Forms of management: hierarchical, project mode, management without authority, autonomous groups.

Main management entry points:
- at the level of the individual: approach to behavior and communication through Transactional Analysis
- at group level: Sainsaulieu models.

Visions of management since the beginning of the 20th century: Mayo, Herzberg, Maslow, MacGregor, Lewin, ...

Leadership styles: from Blake to Mouton to Hersey and Blanchard; the concept of situational management.
Reading list :

10 h Lecture

921155

Public speaking

10 h

Goals to be achieved :
Goals

The course aims to:
• practice oral expression
• understand their emotions and channel them when speaking
• develop their listening and reformulation skills
• know how to use your spontaneity
• learn to be assertive in order to communicate better.
.
Skills developed

At the end of this course, students will have developed skills in:
• integrate into an organization
• take into account professional issues
• take into account and enforce societal values
• make professional choices and integrate into professional life
Course details :
to be adapted according to the speaker (participatory training, theoretical contributions, simulations, fun exercises, etc.) in order to develop the targeted skills
Reading list :

10 h Tutorial

UE 5-5 Languages

48h

ECTS

Hours

921200

UE 5-5 Languages

48 h

921210

English for engineers

1.5

24 h

Reading list :
Dictionnaires de langue générale

24 h Tutorial

921220

Language

1.5

24 h

24 h Tutorial

921221

German

1.5

24 h Tutorial

921222

Spanish

1.5

24 h Tutorial

921223

French

1.5

24 h Tutorial

921224

English booster

1.5

24 h Tutorial

921225

Portuguese

1.5

24 h Tutorial

921226

Japanese

1.5

24 h Tutorial

921227

Italian

1.5

24 h Tutorial

921228

German, complete beginner

1.5

24 h Tutorial

921229

Spanish, complete beginner

1.5

24 h Tutorial

UE 6-1 Probability and Statistics

112h

ECTS

Hours

112100

UE 6-1 Probability and Statistics

112 h

112110

Probability 3

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

112120

Statistical Inference

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

112130

Linear regression

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

UE 6-2 Decision Making

94h

ECTS

Hours

112200

UE 6-2 Decision Making

94 h

112210

Numerical linear algebra

42 h

Prerequisites :
111210|111220|111130
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

112220

Graph theory and Combinatorics

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

112230

Data Mining

22 h

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

UE 6-3 Computing

158h

ECTS

Hours

112300

UE 6-3 Computing

158 h

112310

Initiation to git

4 h

Prerequisites :
111210
Goals to be achieved :
* Discover and understand version management tools.
* Know how to use the basic functions of the GIT tool
Course details :
Through the GIT tool, the student will be able to create a GIT project, to develop new versions, to resolve possible conflicts during a collaborative development.

This 4 hour course will be divided into two parts:
A 2-hour lecture to understand the basic commands.
A practical part which is used to apply the principles and commands.
Reading list :

2 h Lecture
2 h Practical

112320

Data Structures and Algorithms

50 h

Prerequisites :
111210|112310
Goals to be achieved :
* Know the main data structures (organization of data, their main primitives and complexities). * Be able to make prototypes of these data structures in a programming language (language elements, abstract type, implementation)
* Be able to use a programming environment (debugger, memory profiler, modular compilation)
Course details :
The structures studied are: stacks, queues, priority queues, dictionaries (binary search trees, hash tables). The notions of complexity studied are: complexity in the best case, the worst case, on average. The language points (C) addressed are: dynamic allocation, separate compilation (header files and source file), recursion on trees.
Reading list :
Introduction à l'algorithmique. Cormen, Leiserson, Rivest, Stein. Dunod.
Analyse d'algorithmes. Flajolet, Sedgewick

14 h Lecture
18 h Tutorial
18 h Practical
1 h DS

112330

Object-oriented Programming

30 h

Prerequisites :
111210|112320
Goals to be achieved :
- Identify and explain the fundamental concepts of object-oriented programming (classes, objects, encapsulation, inheritance, polymorphism).
- Apply object-oriented programming concepts in the context of statically typed languages.
- Analyze the contribution of object-oriented programming to high-quality software development (modularity, extensibility, maintainability).
- Design and implement solutions in Java using object-oriented programming concepts and UML modeling.
Course details :
- Basic concepts: object, class, message, subclass, and inheritance
- Polymorphism, static and dynamic typing
- Specialization and abstraction. Abstract classes, interfaces
- Exceptions in Java

12 h Lecture
18 h Practical
1 h DS

112340

Automata and grammars

16 h

Prerequisites :
111210
Goals to be achieved :
* Know the basics of language theory
* Be able to manipulate data files in text format (json, XML)
Course details :
Course:
-Phases of a compiler
-Lexical analysis: regular expressions, finite state automata;
-Syntax analysis: context-free grammars;
-Text-based data format (json and XML): Formats, lexical and syntax analysis;
- Applications: Analyzers.

Labs: Application of the concepts seen in course to parse files in XML and JSON format.
Reading list :

10 h Lecture
6 h Practical
1 h DS

112350

Advanced data bases

18 h

Prerequisites :
111310|111320|111210
Goals to be achieved :
* Understand how transactions work using locks and isolation levels
* Be able to implement functions and stored procedures and set up triggers to automate the calling of these functions within the SQL database
* Be familiar with the data structures used by SQL databases
* Recognize which queries can be optimized and be able to set up an index to optimize these SQL queries
Course details :
* Stored procedures and triggers
* Transactions
* The use of indexes in SQL
Reading list :

4 h Lecture
2 h Tutorial
12 h Practical

112360

Internet

24 h

Prerequisites :
111310|111320|112340|111210
Goals to be achieved :
* Know the principle of a client-server architecture
* Know the TCP-IP model, focusing on the upper layer called "application".
* Know the WEB protocol
* Know how a web application is structured and the associated technologies
* Know how to retrieve data, integrate it into a web page and use javascript libraries to display it.
Course details :
- History of networks
- Basics of Javascript and web languages
- Visualization of encapsulation and web protocols
- Data integration and analysis
Reading list :

8 h Lecture
16 h Practical

112370

Operating Systems

16 h

Prerequisites :
111210
Goals to be achieved :
* Understand the role of an operating system
* Be able to interact with the system using the command interpreter and master common commands
* Know the process management: understand the role and life cycle of a process, as well as classic scheduling algorithms
* Know how to manage disks: know the principles of the file management system, be able to use input/output functions on files in C
* Know memory management: know the mechanisms of memory allocation, virtual memory, paging, segmentation
Course details :
The objectives of this module are the following:
* to interact with the OS through the command-line interface and to use usual commands;
* process management: to know the role of processes and their lifecycle, and classic scheduling algorithms;
* disk management: to know the principles of a file manager, to be able to use input/output functions in C;
* memory management: to understand memory allocation, virtual memory, pagination, segmentation.
Reading list :

o Andrew Tanenbaum. Systèmes d'exploitation. Pearson, 2008, 3e édition
o Daniel P. Bovet and Marco Cesati. Le noyau Linux. O'Reilly, 2001.

8 h Lecture
8 h Practical
1 h DS

UE 6-4 Project IS and Soft Skills

46h

ECTS

Hours

112400

UE 6-4 Project IS and Soft Skills

46 h

112410

Projet Graph for Optimization

14 h

Prerequisites :
112220|112320
Goals to be achieved :
* Analyze, design and program in C an application using complex data structures to solve a graph problem
Course details :
This project is the synthesis of several modules (algorithmic, data structures, graphs and combinatoric)

The project is carried out in twos.
The project is divided into 2 phases: analysis/modeling and implementation.
After 4 hours of sessions, students hand in an analysis/modeling report.
Each pair receives feedback from its tutor before the second phase, when the solution is developed.

At the end of the project, each student must assess himself/herself on the following points: consideration of specifications / project modeling / implementation, code quality, file organization / application operating instructions / report writing.
The final score is calculated from 10 and a weighting of the above elements.
A bonus/malus is awarded for good/bad self-assessment.
Reading list :

14 h Project

112420

Project Data WEB

10 h

Prerequisites :
112360|111220|111250
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

921150

Communication tools

10 h

Goals to be achieved :
he course has the following general objectives:
- understand the challenges of communication for the engineer of tomorrow
- know how to communicate effectively his professional project in writing and orally
- communicate to convince

Skills

At the end of this course, students will have developed skills in:
- integrate into an organization: self-knowledge, team spirit, communication with specialists as well as with non-specialists, etc.
- take into account professional issues
- make professional choices and integrate into professional life.
Course details :
Effectively communicate your professional project: new job search techniques (CV, motivation email, e-recruitment and social networks).
Communicate to convince in business: the example of e-mail
An essential tool at university and in business: the slideshow
Reading list :

10 h Tutorial

922410

Risk prevention in business

4 h

Goals to be achieved :
Identify the general principles of prevention
Identify the links between work and health

Understand the observation grid to be appended to the 3A activity report
Course details :
Evaluate the level of maturity of a company in terms of OHS through a serious game developed by INRS
Reading list :

1 h Lecture

922420

Digital culture and data security

8 h

Goals to be achieved :
-•Be aware of:
- risks (personal/confidentiality/economic/societal)
- the possibilities/limitations of digital tools
- your own cognitive biases.
•Understand the nature of data and its impact.
•Know, understand, and apply the university's charter on ISAGs.
•Have the necessary knowledge to understand how computers/algorithms/artificial intelligence work.
•Choose your digital tools in a reasoned manner and be able to justify your choice
Course details :
Summary program defined by each specialty
Reading list :

UE 6-5 Languages

48h

ECTS

Hours

922100

UE 6-5 Languages

48 h

922110

English for engineers

1.5

24 h

Prerequisites :
921210
Reading list :
Dictionnaires de langue générale

24 h Tutorial

922120

Language

1.5

24 h

24 h Tutorial

922121

German

1.5

Prerequisites :
921221

24 h Tutorial

922122

Spanish

1.5

Prerequisites :
921222

24 h Tutorial

922123

French

1.5

24 h Tutorial

922124

English booster

1.5

24 h Tutorial

922125

Portuguese

24 h Tutorial

922126

Japanese

1.5

24 h Tutorial

922127

Italian

1.5

24 h Tutorial

922128

German, complete beginner

1.5

24 h Tutorial

922129

Spanish, complete beginner

1.5

24 h Tutorial

UE 7-1 Statistical Modelling

100h

ECTS

Hours

113100

UE 7-1 Statistical Modelling

100 h

113110

Exploratory statistics

2.5

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

113120

Supervised classification

1.5

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

113130

Markovian random models

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

UE 7-2 Decision making

52h

ECTS

Hours

113200

UE 7-2 Decision making

52 h

113210

Numerical computing for optimisation

1.5

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

113220

Operational research

1.5

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

Practical part
- Modeling and solving complex problems using Lingo/Cplex
- Analysis and exploitation of results
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

UE 7-3 Software Engineering and Information Systems

58h

ECTS

Hours

113300

UE 7-3 Software Engineering and Information Systems

58 h

113310

Object-oriented programming

14 h

Prerequisites :
112330
Goals to be achieved :
- Identify and explain the fundamental concepts of object-oriented programming (classes, objects, encapsulation, inheritance, polymorphism).
- Apply object-oriented programming concepts in the context of statically typed languages.
- Analyze the contribution of object-oriented programming to high-quality software development (modularity, extensibility, maintainability).
- Design and implement solutions in Java using object-oriented programming concepts and UML modeling.
Course details :
- Parametric generics
- Generic collections and utilities
- Lambda expressions and stream processing
- Files and object serialization
- Event-driven programming and graphical user interfaces
Reading list :

6 h Lecture
8 h Practical

113320

Object-oriented information systems

12 h

Prerequisites :
112330|112350
Goals to be achieved :
* Be able to model object-based information systems using UML diagrams
Course details :
diagram studies :
- use cases
- sequences
- state-transition
- collaboration diagrams
- ...
Reading list :

4 h Lecture
6 h Tutorial
2 h Practical
1 h DS

113330

Testing and maintaining

12 h

Prerequisites :
113310
Goals to be achieved :
* Understanding the importance of maintenance
* Knowing the test mechanism
* Developping tests first
* Better understanding the object paradigm
* Knowing the foundations of software quality
* Knowing the rudiments of visualization
* Discovering the continuous integration principles
* Studying the quality of unknown software
* Enhancing your own development
Course details :
- (Course) General introduction
- (Course) Testing and test-driven development
- (Course) Continuous integration and clean code
- (Practical) Unit testing
- (Practical) Continuous integration pipeline
- Clean Code project
Reading list :
April, A., Huffman Hayes, J., Abran, A., & Dumke, R. (2005). Software Maintenance Maturity Model (SMmm): the software maintenance process model. Journal of Software Maintenance and Evolution: Research and Practice, 17(3), 197–223. https://doi.org/10.1002/smr.311

Bendifallah, S. (1987). Understanding Software Maintenance Work. IEEE Transactions on Software Engineering, SE-13(3), 311–323. https://doi.org/10.1109/tse.1987.233162

Herrin, W. R. (1985). Software maintenance costs. ACM SIGCSE Bulletin, 17(1), 233–237. https://doi.org/10.1145/323275.323383

Leach, R. J. (1990). Software metrics and software maintenance. Journal of Software Maintenance: Research and Practice, 2(2), 133–142. https://doi.org/10.1002/smr.4360020204

Parikh, G. (1985). Software maintenance. ACM SIGSOFT Software Engineering Notes, 10(5), 89–98. https://doi.org/10.1145/382288.382774

Seaman, C. B. (2000). Practical Software Maintenance. Journal of Software Maintenance: Research and Practice, 12(4), 249–253. https://doi.org/10.1002/1096-908x(200007/08)12:4%3C249::aid-smr213%3E3.0.co;2-n

Wee, N.-S. (1990). Optimal Maintenance Schedules of Computer Software. Probability in the Engineering and Informational Sciences, 4(2), 243–255. https://doi.org/10.1017/s026996480000156x

6 h Lecture
6 h Practical

113340

Introduction to virtual environments

4 h

Goals to be achieved :
At the end of this course, students will be able to:
- Explain the role of virtual environments in dependency management and the reproducibility of Python projects.
- Install and manage dependencies within an isolated environment.
- Choose an environment management tool appropriate for the context of a development or data science project.
Course details :
Introduction to Python virtual environments and their role in dependency isolation and project reproducibility. Presentation and use of environment management tools (venv, virtualenv, conda, poetry), with a brief comparison to the concept of virtual machines.
Reading list :

4 h Practical

113350

Concurrent programming

16 h

Prerequisites :
112370
Goals to be achieved :
* Know multi-threaded programming techniques
* Know concurrent programming techniques: semaphore, synchronization
Course details :
1- Inter-process concurrence, Process management (fork, exec, wait) - (Tutorial and Practical exercises)
2 - Intra-process concurrence, Lightweight processes (Posix threads) - (Tutorial and Practical exercises)
3 - Shared resource management, mutual exclusion, semaphores - (Tutorial and Practical exercises)
Reading list :
* La programmation sous Unix - 3ème édition - J.M. Riflet - Ediscience International
* Systèmes d'exploitation. A. Tanenbaum - Inter Editions

6 h Lecture
10 h Practical
1 h DS

UE 7-4 Soft Skills

142h

ECTS

Hours

113400

UE 7-4 Soft Skills

142 h

113410

Initiation to Research

10 h

Prerequisites :
None
Goals to be achieved :
* Know the basic principles of some key theories in epistemology
* Know some major research programs for mathematics and computer science
* Know some basic principles on scientific practices in mathematics and computer science, at the beginning of the twenty-first century
Course details :
1. Introduction to epistemology: inductivism, falsificationism, structure of scientific revolutions (theory of Thomas Kuhn).
2. Crisis in mathematics and the birth of computer science: the halting problem of algorithms, Turing's machines as models of algorithms, Church's thesis; the crisis over the notion of truth in mathematics, the axiomatization of theories, Hilbert's program, the Entscheidungsproblem, Hilbert's second and tenth problems.
3. Practices of "normal science" in computer science: structure of research articles, bibliography, conferences, journals, indicators for the evaluation of articles, researchers and journals (Hirsch index, impact factor).
Reading list :

- Qu'est-ce que la Science ? (Chalmars).- La Structure des Révolutions Scientifiques (Kuhn).- La révolution copernicienne (Kuhn).- Six études de psychologie (Piaget).- Éléments d'histoire des mathématiques (Bourbaki).- Le théorème de Gödel (Nagel, Newman, Gödel, Girard).- Alan Turing ou l'énigme de l'intelligence (Hodges).- Le dixième problème de Hilbert. Son indécidabilité (Matiiassevitch).- Physique quantique et représentation du monde (Schrödinger).- De nombreux articles de Jean-Paul Delahaye dans Pour la Science.

6 h Lecture
4 h Project
1 h DS

113420

Computer science and sustainable development

10 h

Goals to be achieved :
* Be aware of the impact of digital technology on the environment
Course details :
- Overview of the direct and indirect environmental impacts of digital technology
- Awareness of life cycle analysis and eco-design of digital services
Reading list :
ADEME, Shift project

2 h Lecture
4 h Tutorial
4 h Practical

113430

Project Markovian Random Models

1.5

16 h

16 h Project

113440

Project Operationnal research

1.5

12 h

12 h Project

113450

Project Object-oriented Algorithmics

20 h

Prerequisites :
113310
Goals to be achieved :
- Design and model an application using object-oriented programming and UML diagrams.
- Structure an application by clearly distinguishing between the functional core and the user interface (UI).
- Implement the functional core in Java in a modular manner that is independent of the user interface.
- Write and run unit tests to validate the behavior of the functional core.
- Develop a user interface (UI) based on the functional core.
Course details :
Design and development of an application using Java object-oriented programming as a semi-autonomous project, with UML modeling, separation of functional core/user interface, implementation of unit tests, and introduction to code maintenance and improvement practices.

20 h Project

113460

Project Management of IT Systems

36 h

12 h Lecture
12 h Tutorial
12 h Project
1 h DS

921145

SHEJS_Manager Human Resources

10 h

Goals to be achieved :
Goals
The course aims to enable students to:
- know the basic concepts of the discipline
- acquire a marketing mindset in order to take the customer into account in
any production and/or design process.
Skills acquired at the end of the course
At the end of this course, students will have developed skills in:
- analyze the challenges of value creation
- dialogue and work with professionals in the discipline
- understand and anticipate changes in the environment.
Course details :
Introduction: the marketing concept and its evolution
I- Market analysis
- knowledge of the markets and their players
- market research
II- The marketing strategy
- the strategic diagnosis
- strategic choices
III- Marketing means of action
- the offer
- the price
- the communication of the offer
- market access
Reading list :

923310

SHEJS_S7_Business management challenge

28 h

Goals to be achieved :
Goals
The 3rd year activity allows the engineering student to enrich his knowledge
of the business world. This experience helps him reflect on his choices
guidance and his future career as an engineer.
The module inherent in this activity is a written communication exercise
and oral.

Skills acquired at the end of the course
Knowledge of the business world
- Know how to analyze the economic environment of the company
- Understand the development strategy of a company
- Know how to position yourself in the organization of a company

Communication
- Know how to write a report
- Know how to present your results orally
- Know how to express and defend a point of view
- Knowing how to assert oneself in a group
- Know how to use a slideshow tool (Powerpoint, Prezi...)

Personal and cultural dimension
- Know how to define your professional project
- Know how to integrate into an organization, a team
- Knowing how to place your responsibilities
Course details :
Written communication
Write a structured activity report in accordance with substantive instructions and
of shape.

Oral communication
Present this experience orally by highlighting the skills
Reading list :

1 h Lecture
4 h Practical

UE 7-5 Languages

48h

ECTS

Hours

923200

UE 7-5 Languages

48 h

923210

S7_Specialist and business english

1.5

24 h

Prerequisites :
922110
Reading list :
Dictionnaire général et spécialisé

24 h Tutorial

923220

Language

1.5

24 h

24 h Tutorial

923221

German

1.5

Prerequisites :
922121

24 h Tutorial

923222

Spanish

1.5

Prerequisites :
922122

24 h Tutorial

923223

French

1.5

24 h Tutorial

923224

English support

1.5

24 h Tutorial

923225

Italian

1.5

24 h Tutorial

923226

Japanese

1.5

24 h Tutorial

923227

German, complete beginner

1.5

24 h Tutorial

923228

Spanish, complete beginner

1.5

24 h Tutorial

923229

Portuguese

1.5

24 h Tutorial

UE 8-1 - Statistics and Decision Making

82h

ECTS

Hours

114100

UE 8-1 - Statistics and Decision Making

82 h

114110

Advanced Statistical Modelling

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

114120

Time Series

24 h

10 h Lecture
14 h Practical
1 h DS

114130

Machine learning

22 h

Prerequisites :
113110|113120|113210
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

114140

Data Privacy

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

UE 8-2 - Software Systems and Databases

70h

ECTS

Hours

114200

UE 8-2 - Software Systems and Databases

70 h

114210

IT Systems Architecture

2.5

34 h

Prerequisites :
112330|112340|112350|113320|112360
Goals to be achieved :
- be able to understand the role of an application server and its inner mechanism (introspection and dynamic invocation mechanisms)
- be able to understand the characteristics of some design patterns: proxy, adapter, model-view-controller, and how to apply them in a distributed application
- be able to understand the different kinds of components in a 3-tier software architecture, their life cycle and the interactions between components
- know how to develop enterprise Java components
- know how to analyze a problem and propose an appropriate 3-tier software architecture.
- understand the main principles and challenges of cloud computing
Course details :
* From Software Objects to Components :
* Component Oriented Programming (illustrated with Java Beans)
* From software objects to remote objects :
* Concepts of remote objects. (illustrated with Java JEE).
* From Object to Remote Components :
* 3-tier software architectures (illustrated with Java Enterprise)
* First level : Web components (html, applets, jsp, servlets)
* Second level : the services (EJB entity and session)
* Third level : entity (EJB entity, DBMS, legacy applications)
* Web technologies (web 2.0 services, Servlets, JSP, JSTL, EL,JSON, MVC Pattern) inside 3-tier software architectures
* Design of a software architecture
* Study of existing elements : DBMS, used technologies, Operating systems,programming languages
* component specification, required applications servers
* introduction to cloud computing
* Practises : Java beans, and JEE
Reading list :
http://ocaron.polytech-lille.net/enseignement/#IS4

18 h Lecture
16 h Practical
1 h DS

114220

Big Data

16 h

Prerequisites :
112210
Goals to be achieved :
* Know the main methods for calculating the scores of graph nodes, applied to the evaluation of the importance of hyperlinks linking data.
* Be able to use MapReduce as a programming paradigm for distributed massive data computation.
* Know the basics of "data parallel" programming for computing and processing with very large data sets.
* Be able to use several versions of the PageRank method, experimenting on realistic test data.
Course details :
1 - Introduction, data at the center of applications and methods
2 - Classification of very large data, the HITS, PageRank and SALSA methods
3 - The MapReduce programming paradigm, examples
Reading list :

10 h Lecture
6 h Practical
1 h DS

114230

No-SQL databases

1.5

20 h

Prerequisites :
112320|112220|112350
Goals to be achieved :
- Explain the fundamental principles of NoSQL databases.
- Identify and distinguish the different families of NoSQL databases.
- Select an appropriate NoSQL database family based on the requirements of an application.
- Explain the principle of data fragmentation (sharding).
- Describe the MapReduce architecture model and its role in distributed processing.
- Explain the principle of replication, including its advantages and limitations.
- Use the database management systems MongoDB and OrientDB to manipulate data.
- Write and execute simple and complex queries on these systems.
Course details :
The course is divided into three 2-hour lectures:
- Foundations of NoSQL
- Data fragmentation
- Replication
and six 2-hour lab sessions:
- MongoDB and graphical interface
- MongoDB, aggregation framework, and console
- MongoDB, Python, and data visualization
- OrientDB, links and MATCH queries

6 h Lecture
14 h Practical

UE 8-3 Projects IS

64h

ECTS

Hours

114300

UE 8-3 Projects IS

64 h

114310

Project of Advanced Statistical Modelling

16 h

16 h Project

114320

Project of Machine Learning

10 h

10 h Project

114330

Project of Software Architecture

14 h

Prerequisites :
114210
Goals to be achieved :
* Be able to analyze a problem and propose a suitable three-tier software architecture.
* Be able to apply software engineering best practices (MVC patterns, documentation, code organization)
* Be able to develop Java Enterprise Edition (JEE) components
* Be able to manage a project (time management, teamwork)
Course details :
Students are provided with a set of specifications for a software application to be developed.

Following a functional analysis, students design a software solution using JEE (Java Enterprise Edition) technology, adhering to the principles of a three-tier client-server architecture. This ensures a clear separation between data (entities), business logic (sessions), and the web component by applying the MVC (Model-View-Controller) design pattern. Project management skills are required for this project (time management, team management)

14 h Practical

114340

Project Management of IT Systems

24 h

24 h Project

UE 8-4 - Assistant Engineer Placement : Starting in mid-May (10-13 weeks)

120h

UE 8-5 Languages

48h

ECTS

Hours

924100

UE 8-5 Languages

48 h

924110

S8_English for engineers

1.5

24 h

Reading list :
Dictionnaire général et de spécialité

24 h Tutorial

924120

Language

1.5

24 h

24 h Tutorial

924121

German

1.5

24 h Tutorial

924122

Spanish

1.5

24 h Tutorial

924123

French

1.5

24 h Tutorial

924124

New module name

1.5

24 h Tutorial

924125

Italian

1.5

24 h Tutorial

924126

Japanese

1.5

24 h Tutorial

924127

German, complete beginner

1.5

24 h Tutorial

924128

Spanish, complete beginner

1.5

24 h Tutorial

924129

Portuguese

1.5

24 h Tutorial

UE 9-1 - Specialisation courses

60h

ECTS

Hours

115100

UE 9-1 - Specialisation courses

60 h

115110

Introduction to Deep Learning

22 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
Reading list :
* Gourieroux C. et Monfort A. : Cours de Séries Temporelles, Economica.

* Applications réalisées à l'aide du logiciel R.

10 h Practical
1 h DS

115120

Ontologies

16 h

Prerequisites :
113340
Goals to be achieved :
* Being able to create an ontology to model the concepts and relationships of a knowledge domain.
* Know how to use an ontology to create a knowledge base.
* Know how to manipulate and query data in graph database.
Course details :
- Introduction to the concept of ontology in computer science and knowledge bases
- Modeling and creation of an ontology in OWL
- Graph databases, knowledge graphs
Reading list :

8 h Practical
1 h DS

115130

Data Engineering

22 h

Prerequisites :
113340|114130
Goals to be achieved :
* Know the methods for ingesting data from several different types of source
* Know how to manipulate data and prepare it for its final use
* Know the different data storage areas
Course details :
Although the Data Engineer does not directly create value from data, he/she is nonetheless an essential profile for all data teams, as it is he/she who implements the tools and technical processes required for the preparation and manipulation of data by other team members (Data Scientist, Data Analyst, etc.). The objectives of the introduction to data engineering module are to address both theoretically and practically the major concepts and tools of this profession, in particular by tackling the following points:
- Data retrieval: How do you ingest data from several different types of source? Which ingestion method for which type of data (batch ingestion, real-time, etc.)?
- Data transformation: How do you manipulate data to standardise it and prepare it for different business uses?
- Data storage: How can data be stored in data lakes and data warehouses to make it usable by other data team members?
Reading list :

UE 9-2 - Elective Modules : 4 courses to be chosen from the following list

88h

ECTS

Hours

115200

UE 9-2 - Elective Modules : 4 courses to be chosen from the following list

88 h

115210

Advanced optimization methods

Prerequisites :
113220
Goals to be achieved :
* Know the different families of difficult and complex optimization problems, which are close to real problems encountered in fields as varied as production management, logistics and transport, and investment planning.
* Understand the main optimization methods based on heuristics.
* Be able to understand and analyze a problem
* Be able to design and implement a suitable solving method
* Know and be able to use methods for validating and comparing solving methods.
Course details :
* Modeling and analysis of difficult optimization problems
* Metaheuristics:
* Presentation: advanced local search, evolutionary algorithms, swarm intelligence, etc.
* Design and Implementation techniques
* Multi-criteria decision making and optimization :
* Illustration of typical problems
* Main techniques of optimization and decision making
* Game theory
Reading list :
E-G. Talbi, "Metaheuristics: from design to implementation", Wiley.

10 h Practical

115220

Statistical Methods for Marketing

Goals to be achieved :
* Know the basic principles of customer marketing and statistical methods to answer a business problem, both in the algorithmic and analytical approach
* Be able to identify the methodological and restitution reflexes expected in companies
Course details :
1. Principles of customer marketing: loyalty/prospecting, segmented marketing plan, what data for marketing, loyalty programs, geomarketing, tools: segmentations (RFM, PMG...), typologies, scorings, NLP.
2. Move from a business problem to a statistical tool or approach to respond to the problem posed
- Construction of an operational customer segmentation
- learning supervised modeling of the "logistic regression" type and valorization of the business contribution for the company of this type of tool
Reading list :

Logiciels: SPSS, SPAD, SAS, Excel, KXEN

3 h Tutorial

115230

MLOPS

Prerequisites :
114210
Course details :
* Software categories: libraries, frameworks, design patterns, software models, meta-level architectures (MDA, Model Driven Architectures), plugin-based architectures (Eclipse)
* Standard Design Patterns
* Model-Driven Engineering (MDE). Metamodels (UML, MOF), OCL. Model verification and transformation, code generation
* Practical work consisting in progressively implementing an automatic MDE software chain starting from a UML metamodel to several technological choices, for example UML to SQL, UML to EJB, ...
* Environment: Eclipse (Eclipse Metamodeling Framework) and its plugins, UML, MTL, OCL, ...
Reading list :
* "Design Patterns" E. Gamma, R. Helm, R. Johnson, J. Vlissides. Editions Vuibert
* "Head First Design Patterns" E & E Freeman. Ed. O'Reilly
* " Design patterns : descriptions et solutions illustrées en UML 2 et Java" Laurent Debrauwer. Editions ENI
* "OO Re-engineering Patterns" S. Demeyer, S. Ducasse, O. Nierstrasz. Square Bracket Associates Editors
* "Ingénierie dirigée par les modèles" J.M. Favre, J. Estublier, Mireille Blay-Fornarino. Editions Hermes Lavoisier

115240

Data visualisation

Prerequisites :
113110|111220|111250
Goals to be achieved :
* Know and be able to use different data visualization techniques to effectively communicate complex information.
* Know how to use Python and Tableau Software
Course details :
The program includes lessons on the basic principles of data visualization, understanding the basics of a good visualization to choose the right graphic and customize it, organizing a project to create a dashboard (to avoid the pitfalls of the project you never see the end of in companies!), identifying the steps involved in creating a dashboard as well as the different tools and technologies available on the market to implement them (Tableau Software, Python).

115250

Natural Language Processing

Prerequisites :
114130
Reading list :

UE 9-3 - Accounting, Management, Communication 3

75h

ECTS

Hours

115300

UE 9-3 - Accounting, Management, Communication 3

75 h

925510

Cross-disciplinary modules

24 h

925595

Economics, geopolitics and international geostrategy

Reading list :

925639

Art & Sciences

Reading list :
Site FOOR : Relais des collaborations Art/Sciences-Technologies en Hauts-de-France et Belgique
Site du Programme Images, sciences et technologie Prist
Derniers ouvrages réalisés par l’Esä dans le cadre de Prist qui présentent les projets des étudiants art.
Catalogue d’exposition Master Mind, Ecole supérieure d’art du Nord-pas-de-Calais (Esä), 2016 Catalogue Master Mind
Catalogue d’exposition Cells Fiction, Edition Espace Croisé, 2017 Catalogue Cells Fiction
Catalogue d’exposition Collisions, Ecole supérieure d’art du Nord-pas-de-Calais (Esä), 2018 Catalogue Collisions

925755

Climate emergency and engineering

Reading list :

925756

Climate change and its impacts

Goals to be achieved :
- Understand the basics of the climate system mechanisms
- Understand the difference between climate variability and climate change
- Understand climate modeling and its uncertainties
- Understand risks induces by climate change
Course details :
This course intends to explain the climate system mechanisms and the various climate changes that have affected our planet over time. A focus will be made on current climate change and its causes, and also on the future climate we can expect and its uncertainties. Finally, the expected risks related to climate change will be discussed.

The course will cover the following topics:
• Introduction of the climate system mechanisms
• Climate variability over time
• The causes of climate variability
• The current climate change and the future climate
• Risks induces by climate change

925757

Renewable Energy and Electrochemical Energy Storage

Goals to be achieved :
- Students will understand the basics of energy conversion and energy storage
- Students will know the basics of each renewable energy sources
- Students will know the basics of each energy storage technologies
- Students will know the basics of battery and supercapacitor technologies
Course details :
This course provides the students with an introduction to the different renewable energy sources, including hydropower, solar energy, wind energy, bioenergy, and geothermal energy. The pros and cons, challenges of each renewable energy sources will be discussed. Due to the sporadic nature of most of the renewable energy sources, the energy storage devices are important for electrifying the economy. Different energy storage technologies will be introduced with focusing on electrochemical energy storage technologies.

The course will cover the following topics:
• Introduction of different energy sources: renewable vs non-renewable energy sources
• The basics of different renewable energy sources (hydropower, solar energy, wind energy, bioenergy, and geothermal energy)
• The basic of different energy storage technologies (thermal, mechanical, chemical and electrochemical energy storage)
• The basics of electrochemical energy storage devices – supercapacitors vs batteries
• Beyond Li-ion technologies

925610

How to manage at the service of operational and social performance

24 h

Goals to be achieved :
Objectifs
Les fondamentaux du management d’équipe seront perfectionnés et mis à l’épreuve de situations vécues ou observées (retour d’expérience).
L’objectif pédagogique est de permettre à l’étudiant d’appréhender des situations de management d’équipe et de disposer de process d’aide à la décision, à l’organisation d’équipe.
L'intention pédagogique est aussi d'intégrer dans ses pratiques professionnelles de manager, la dimension sociale au service de la performance stratégique.
Enfin le module vise à proposer une réflexion sur le pourquoi et le
comment « agir au mieux dans nos actes professionnels » afin de diminuer
les risques éthiques individuels et pour l’entreprise.
Course details :
The essentials of management
- the job of manager (integration and legitimacy)
- personal and team organization
- employee assessment and development methods
- the mobilization of a collective (meeting facilitation, motivation, question of leadership)
- the types of manager (adaptation, delegation, etc.)

Regarding social performance
- identify the points of attention of the social performance
- understand the evaluation mechanisms and the issues
- act on dysfunctions

About ethics
This course aims to:
- discover and appropriate the elements of the IESF ethical charter
(Society of Engineers & Scientists of France)
- raise awareness of the prevention of discrimination and harassment
morality of a discriminatory nature in a professional situation
Course content
- stereotypes and prejudices
- from constitutional equality to equal opportunity
- the management of ethical risks for fewer dilemmas
ethical
Reading list :

8 h Lecture
16 h Tutorial

925620

Legal issues of the employment relationship

16 h

Goals to be achieved :
Goals
The course aims to enable students to:
- understand the legal rules and principles applicable to the subject
- analyze articles of law and court decisions by isolating the
legal principles they contain
- expose and explain the legal concepts developed in class.

Skills acquired at the end of the course
At the end of this course, students will have developed skills in:
- to integrate into an organization, to animate it and to make it evolve
- take into account professional issues such as respect for
procedures
- take into account and enforce societal values
- fit into professional life
Course details :
The sources and actors of labor law
Candidate recruitment and hiring
The work contract
- the characteristic elements of the employment contract
- other forms of commitment
The fixed-term employment contract
- analysis, comments and practice of the fixed-term contract
The term of the open-ended employment contract
- the choice of failure modes
Employee representation
Responsibilities in terms of employee health and safety
- INRS e-learning "health and safety at work"
- simulation of the animation of a quarter of an hour safety
Reading list :

12 h Lecture
4 h Tutorial

925630

Enhance your skills

11 h

3 h Lecture
8 h Tutorial

925640

Apprentice Project

51 h

51 h Project

UE 9-4 - Final year Project

100h

Formative assessment

20h

ECTS

Hours

925300

Formative assessment

20 h

925310

CP - feedback

8 h

Reading list :

8 h Tutorial

925320

CP - cultural communication

12 h

Prerequisites :
921220|923220|922120|924120|925110|925120
Goals to be achieved :
Targeted Learning Outcomes
Take into account the issues of work relations, ethics, safety and health at work
Integrate into an organization, lead it and help it evolve: exercise of responsibility, team spirit, commitment and leadership, project management.
Question yourself in order to analyze and evaluate your actions, your managerial practices and actions related to your practice throughout your professional career.
Analyze the management practices of your peers.

Educational innovation
Team management and group interaction will be perfected and put to the test as part of the StratiRac business game. This management simulator is played in teams of 6 or 7 who reproduce a real CODIR, which takes over a company in difficulty.
This serious game therefore reproduces a potentially stressful situation close to the reality of a business: business to turn around, large volume of documents to understand, organization and team strategy to put in place.
The increase in skills is carried out using reverse teaching, but the students are supervised and guided gradually via various educational workshops of 30 minutes maximum.
The seminar is divided into 2 calendar periods, which makes it possible to challenge students in terms of a reflective approach, and to improve their cooperation.
Theoretical and methodological contributions will be made from these analyzes of practices.

The educational objectives are:
- Allow the student to understand complex management and cooperation situations via the context (the CODIR takes over a company in difficulty at the start of the game, short decision-making times, etc.)
- Develop skills specific to teamwork, work on cooperation, organization, delegation of tasks and report on them
- Integrate the social and ethical dimension in the service of strategic performance
- Integrate into this organization: self-knowledge, team spirit
- Know how to evaluate and analyze one's actions, one's managerial practices, in a professional context

Targeted Sustainable Development Goals: SDG 3, 5, 8 and 16
Course details :
Objective: Understand the complexity and dynamics of communication in situations of intercultural interaction and exchange.

Contents:
Introduction to the fundamental concepts underlying the field of
intercultural communication.
Analysis of intercultural communication situations and the cultural components of
communication: socio-spatial context, frame of reference, tacit, implicit and
explicit knowledge, etc. Introduction to some common pitfalls in intercultural communication:
language, social norms, roles, non-verbal codes, traditions, gender perceptions
, prejudices, stereotypes, etc.
Analysis of adaptation and communication processes. Identity issues
and the experience of culture shock, dynamics of openness and closure in the face of otherness,
conflict resolution strategies, representations of the other, and definitions of
intercultural competence.
Reading list :

12 h Tutorial

UE 9-5 Languages

40h

ECTS

Hours

925100

UE 9-5 Languages

40 h

925110

English

20 h

Prerequisites :
924110
Reading list :
* General and specialised language dictionaries
* PC équipé dun Pack Office ou équivalent

20 h Tutorial

925120

Second language

20 h

20 h Tutorial

925121

German

Prerequisites :
924121

20 h Project

925122

Spanish

Prerequisites :
924122

20 h Tutorial

925123

French

20 h Tutorial

Unit 10-1 Engineer placement

400h

ECTS

Hours

926100

Unit 10-1 Engineer placement

400 h

926110

Engineer Placement

400 h

400 h Project

Formative assessment

51h

ECTS

Hours

926200

Formative assessment

51 h

926210

Feedback

8 h

Reading list :

8 h Tutorial

926220

Feedback

8 h

Reading list :

8 h Tutorial

926230

CP - Strategy and operational management: from implementation to reflective analysis 2

35 h

Goals to be achieved :
Targeted Learning Outcomes
Take into account the issues of work relations, ethics, safety and health at work
Integrate into an organization, lead it and help it evolve: exercise of responsibility, team spirit, commitment and leadership, project management.
Question yourself in order to analyze and evaluate your actions, your managerial practices and actions related to your practice throughout your professional career.
Analyze the management practices of your peers.

Educational innovation
Team management and group interaction will be perfected and put to the test as part of the StratiRac business game. This management simulator is played in teams of 6 or 7 who reproduce a real CODIR, which takes over a company in difficulty.
This serious game therefore reproduces a potentially stressful situation close to the reality of a business: business to turn around, large volume of documents to understand, organization and team strategy to put in place.
The increase in skills is carried out using reverse teaching, but the students are supervised and guided gradually via various educational workshops of 30 minutes maximum.
The seminar is divided into 2 calendar periods, which makes it possible to challenge students in terms of a reflective approach, and to improve their cooperation.
Theoretical and methodological contributions will be made from these analyzes of practices.

The educational objectives are:
- Allow the student to understand complex management and cooperation situations via the context (the CODIR takes over a company in difficulty at the start of the game, short decision-making times, etc.)
- Develop skills specific to teamwork, work on cooperation, organization, delegation of tasks and report on them
- Integrate the social and ethical dimension in the service of strategic performance
- Integrate into this organization: self-knowledge, team spirit
- Know how to evaluate and analyze one's actions, one's managerial practices, in a professional context

Targeted Sustainable Development Goals: SDG 3, 5, 8 and 16
Course details :
Themes covered:
Management practices to implement to create team cohesion and produce performance
The different management styles
Practical team management
Group dynamic.
Taking into account individual personalities and behaviors in a work team, managing difficult personalities
Ethics
Social and Environmental Responsibility and Safety at Work
Large group project mode, hybrid teaching
Multidisciplinary approach, development of soft skills (collaboration, problem solving, creativity, critical thinking.)
Reading list :

35 h Tutorial

Enterprising Challenge

35h

Data Engineering and Artificial Intelligence

Activity sectors

Links with research units

Program

Program

UE 5-1 - Fundamental of Mathematics

UE 5-2 Fondamental of Computer Science

UE 5-3 Databases

UE 5-4 Economics, Soft Skills

UE 5-5 Languages

UE 6-1 Probability and Statistics

UE 6-2 Decision Making

UE 6-3 Computing

UE 6-4 Project IS and Soft Skills

UE 6-5 Languages

UE 7-1 Statistical Modelling

UE 7-2 Decision making

UE 7-3 Software Engineering and Information Systems

UE 7-4 Soft Skills

UE 7-5 Languages

UE 8-1 - Statistics and Decision Making

UE 8-2 - Software Systems and Databases

UE 8-3 Projects IS

UE 8-4 - Assistant Engineer Placement : Starting in mid-May (10-13 weeks)

UE 8-5 Languages

UE 9-1 - Specialisation courses

UE 9-2 - Elective Modules : 4 courses to be chosen from the following list

UE 9-3 - Accounting, Management, Communication 3

UE 9-4 - Final year Project

Formative assessment

UE 9-5 Languages

Unit 10-1 Engineer placement

Formative assessment

Enterprising Challenge

@ Polytech-Lille