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MATH 111
Math I
Credits (TN) :
6
Credits (US) :
3
UE
UEF 110
CS 121
Programming I
Credits (TN) :
7
Credits (US) :
3.5
UE
UEF 120
CS 161
Introduction to computer systems
Credits (TN) :
7
Credits (US) :
3.5
UE
UEF 130
CSE 123
Introduction to Digital Systems
Credits (TN) :
6
Credits (US) :
3
UE
UEF 140
ENG 101
Academic English
Credits (TN) :
4
Credits (US) :
2
UE
UET 110
ISS 166
Freshman Seminar
Credits (TN) :
2
Credits (US) :
1
UE

UET 110
MATH 112
Math II
Credits (TN) :
6
Credits (US) :
3
UE
UEF 210
CS 141
Data structures & algorithms
Credits (TN) :
7
Credits (US) :
3.5
UE
UEF 220
CS 203
Introduction to operating systems
Credits (TN) :
7
Credits (US) :
3.5
UE
UEF 230
CS 231
Introduction to databases
Credits (TN) :
4
Credits (US) :
2
UE
UEF 240
ENG 121
English composition
Credits (TN) :

6

Credits (US) :

3

UE
UET 210
COM 131
Public Speaking Seminar
Credits (TN) :

2

Credits (US) :

1

UE
UET 210
MATH 270
Probability & statistics
Credits (TN) :
4
Credits (US) :
2
UE
UEF 310
CS 342
Algorithms & Complexity
Credits (TN) :
4
Credits (US) :
2
UE
UEF 320
CS 328
OO Software design & construction
Credits (TN) :
7
Credits (US) :
3.5
UE
UEF 330
CS 411
Computer Networks
Credits (TN) :
5
Credits (US) :
2.5
UE
UEF 340
COM 225
Business & Technical Communication
Credits (TN) :
4
Credits (US) :
2
UE
UET 310
CS 321
Principles of software engineering
Credits (TN) :
6
Credits (US) :
3
UE
UEO 310
CS 336
Data warehousing & management
Credits (TN) :
4
Credits (US) :
2
UE
UEF 410
CS 425
Web application development
Credits (TN) :
5
Credits (US) :
2.5
UE
UEF 420
CS 428
Software Testing & quality assurance
Credits (TN) :

5

Credits (US) :
2.5
UE

UEF 430
CS 481
Artificial intelligence
Credits (TN) :
4
Credits (US) :
2
UE
UEF 440
PHIL 222
Contemporary Issues in Data Ethics
Credits (TN) :
6
Credits (US) :
3
UE
UET 410
CS 370
Human Computer Interaction
Credits (TN) :
6
Credits (US) :
3
UE
UEO 410
CS 455
Cloud computing
Credits (TN) :
5
Credits (US) :
2.5
UE
UEF 510
CS 426
Mobile application development
Credits (TN) :
5
Credits (US) :
2.5
UE
UEF 520
CS 412
Computer security
Credits (TN) :
4
Credits (US) :
2
UE
UEF 530
CS 423
Software architecture & design patterns
Credits (TN) :
4
Credits (US) :
2
UE
UEF 540
Business & Entrepreneurship Elective
Credits (TN) :
6
Credits (US) :
3
UE
UET 510
MGMT 322
Project management
Credits (TN) :
6
Credits (US) :
3
UE
UEO 510
ISS 321
Capstone project/Internship (Stage de fin d’études)
Credits (TN) :
30
Credits (US) :
15
UE
UEF 610
CS 482
Mathematical foundations of data science
Credits (TN) :
7
Credits (US) :
3.5
UE
UE 1
CS 435
Big data technologies & applications
Credits (TN) :
7
Credits (US) :
3.5
UE
UE 2
CS 431
Data management for data scientists
Credits (TN) :
6
Credits (US) :
3
UE
UE 3
COM 425
Advanced technical communication
Credits (TN) :
6
Credits (US) :
3
UE
UET 4
CS 521
Software engineering for data scientists
Credits (TN) :
4
Credits (US) :
2
UE
UEO 5
CS 483
Machine Learning
Credits (TN) :
7
Credits (US) :
3.5
UE
UE 6
CS 535
Scalable big data processing
Credits (TN) :
7
Credits (US) :
3.5
UE
UE 7
CS 451
Distributed systems
Credits (TN) :
6
Credits (US) :
3
UE
UE 8
COM 435
Effective professional presentations
Credits (TN) :
6
Credits (US) :
3
UE

UET 9
CS 470
Data visualization for data scientists
Credits (TN) :
4
Credits (US) :
2
UE
UEO 10
CS 585
Deep learning and neural networks
Credits (TN) :
7
Credits (US) :
3.5
UE

UE 11
CS 555
Cloud computing for data scientists
Credits (TN) :
7
Credits (US) :
3.5
UE

UE 12
CS 581
Generative AI and large language models
Credits (TN) :
6
Credits (US) :
3
UE
UE 13

PHIL 222

Contemporary issues in Data ethics

Credits (TN) :
6
Credits (US) :
3
UE
UET 14
CS 586
AI for emerging applications
Credits (TN) :

4

Credits (US) :

2

UE
UEO 15
ISS 521
Master Thesis/Project (Mémoire de Stage de fin d’études)
Credits (TN) :
30
Credits (US) :
15
UE
UEF 16

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Course Details :

MATH 111
Math I
Course Description
This course is a first university course in calculus. Topics include functions and inverse functions, limits and continuity, differentiation rules, applications of derivatives including finding extreme values, curve sketching, applied optimization and antiderivatives, definite and indefinite integrals, applications of definite integrals including areas and volumes, and principles of integral evaluation.
Credits (TN) :
6
Credits (US) :
3
UE :
UEF 110

Course Details :

CS 121
Programming I
Course Description
This course is a first introduction to fundamental concepts of procedural programming. The course introduces students to the stored program model of computation, the mapping of real-world scientific and engineering problems into algorithmic solutions, and the logic, syntax, and semantics of imperative programming. Topics include variables and types, expression evaluation, control and iterative structures, arrays, files, and simple recursion. Simple searching and sorting algorithms are also introduced as programming case studies. No prior course in computing or knowledge of programming is required. Basic knowledge of “Bac”-level high-school mathematics is assumed. Python will be used as the programming language.
Credits (TN) :

7

Credits (US) :
3.5
UE :
UEF 120

Course Details :

CS 161
Introduction to computer systems
Course Description
This course introduces the fundamental concepts of computer organization and architecture. It covers the basic building blocks of a computer system, including digital logic, data representation, memory hierarchy, instruction set architecture, and CPU design.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UEF 130

Course Details :

CSE 123

Introduction to Digital Systems
Course Description
This course provides students with an introduction to the design and implementation of digital circuits. It is an entry level course to digital electronics covering the encoding of information, number systems, binary mathematics, logic gates, Boolean algebra, Karnaugh maps and the simplification of Boolean functions, and the design and implementation of digital circuits. This course includes laboratory hands-on sessions in the design, implementation, and testing of digital circuits. The labs are used to reinforce the theoretical concepts discussed in the lectures, with an emphasis on the design, simulation, and testing of digital circuits
Credits (TN) :

6

Credits (US) :

3

UE :

UEF 140

Course Details :

ENG 101
Academic English
Course Description
This course is designed to enable students to communicate in English effectively in an academic and professional context. The course focuses mainly on expanding the students’ vocabulary and introducing them to the different types of language and structures used in academic books and articles. Throughout the course, reading and comprehension, writing, and listening exercises are employed to achieve course objectives. This course also introduces students to the elementary concepts of academic writing such as punctuation, style, and sentence and paragraph structure.
Credits (TN) :
4
Credits (US) :
2
UE :
UET 110

Course Details :

ISS 166
Freshman Seminar
Course Description
This freshman seminar invites students into a dynamic academic journey that blends weekly lectures by guest experts, student-led presentations, and thought-provoking discussions. Spanning a wide range of topics—from the fundamental and applied sciences to the humanities—the seminar introduces students to critical, open-ended questions in the natural sciences and explores their broader implications. Thecourse encourages an appreciation for diverse approaches to human intellectual inquiry and expression, ranging from objective scientific inquiry to subjective and critical perspectives found in the humanities. Structured into four parts—(i) the frontiers of science, (ii) foundations of society (social contract theory), (iii) ethics and sustainability, and (iv) contemporary topics in cultural studies, this course offers a rich interdisciplinary experience. Students will engage with history, philosophy, literature, and cultural studies, fostering a holistic understanding that connects scientific discovery with cultural and ethical contexts. The course emphasizes intellectual curiosity and meaningful dialogue over traditional assessments, utilizing a Pass/Fail grading system to reduce performance pressure.
Credits (TN) :
4
Credits (US) :
1
UE :
UET 110

Course Details :

MATH 112
Math II
Course Description
The first part of this course is a standard second course in calculus, while the second part covers the basics of linear algebra. The first part introduces infinite sequences and series, functions of several variables, partial derivatives and multiple integrals. The second part of this course includes the fundamentals of solving linear equations in linear algebra, matrix algebra and vector space, determinants, eigenvalues and eigenvectors, orthogonality and symmetric matrices.
Credits (TN) :
6
Credits (US) :
3
UE :
UEF 210

Course Details :

CS 141
Data structures & algorithms
Course Description
This course introduces students to fundamental data structures used for storing and searching data. The course aims to provide students with both a theoretical understanding of these data structures, as well as hands-on experience of their implementation. Focus is placed on how these data structures are implemented from scratch in the C language. Students are also introduced to the notations and techniques used in the asymptotic analysis of the algorithms used to manipulate these data structures. The course is organized into two distinct parts. The first part is an overview of the C language. Students are assumed to have had an introductory programming course, but prior knowledge of C is not assumed. Topics include variables, types, operators, expressions, functions, arrays, and pointers. The second part covers data structures and algorithm analysis. Topics include time and space complexity of algorithms, linked lists, stacks, queues, hash tables, and binary search trees.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UEF 220

Course Details :

CS 203
Introduction to operating systems
Course Description
This course is an introductory course to operating systems. After a brief introduction, the course consists of four key parts: (i) process management, (ii) process synchronization, (iii) memory management, and (iv) filesystems. The first part describes the process concept and its implementation in operating systems, threads, inter-process communication, context switching, and processor scheduling. The second part on process synchronization describes how operating systems manage concurrency and resource sharing among concurrent processes. Topics here include race conditions, synchronization constructs, classic synchronization problems, and deadlocks. The third part is dedicated to memory management. It covers virtual memory, dynamic virtual-to-physical address translation, demand-paged virtual memory and page replacement algorithms. The fourth part of the course covers file systems, storage devices, and disk management. Topics here include disk structure and scheduling, filesystem interface, and some aspects of filesystem implementation, especially naming, space allocation and free space management.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UEF 230

Course Details :

CS 231
Introduction to databases
Course Description
This course introduces students to the fundamentals of database management systems. It covers data models, E-R relationship models, relational models and algebra, logical database design, normalization, data organization, and basics of query processing and optimization. Students will learn the various functions and commands of the database as well as how to plan, normalize, create, use and program fully relational databases for common business applications.
Credits (TN) :
4
Credits (US) :
2
UE :
UEF 240

Course Details :

CS 121

English composition

Course Description
English Composition is a course designed to enhance the abilities of students in reading, writing, and critical thinking. The primary goal is to equip students with the skills to read and write effectively, competencies that are foundational for many professional career paths. Strong writing and communication skills are closely tied to critical thinking, analytical reasoning, and effective expression. Students in this course are encouraged to: (i) Read Regularly – reading improves vocabulary, grammar, spelling, punctuation, and writing style; (ii) Master Grammar with Purpose – while understanding grammar rules is essential, good writing goes beyond grammar. Analytical and rhetorical skills are at the heart of effective writing; and (iii) Practice Writing Consistently – writing, like any language, improves with practice. Throughout this course, and by combining frequent reading with disciplined writing, students will accelerate their growth as effective writers and communicators.
Credits (TN) :
6
Credits (US) :
3
UE :
UEF 210

Course Details :

CS 231
Public Speaking Seminar
Course Description
This course is designed to develop effective communication skills through hands-on practice rather than traditional lectures. Students will learn to craft and deliver impactful speeches, choosing topics that inform, persuade, and inspire their audience.
The course follows a progressive approach, gradually introducing more challenging speaking tasks and roles. Through structured exercises, peer feedback, and real-world scenarios, students will build confidence, refine their delivery, and master essential persuasion techniques.
By the end of the course, students will not only enhance their ability to communicate clearly and convincingly but also gain a deeper understanding of their own voice, strengths, and motivations as speakers—crucial skills for engineers in both academic and professional settings
Credits (TN) :
2
Credits (US) :
1
UE :
UET 210

Course Details :

CS 270
Probability & statistics
Course Description
This course is an introduction to probability theory and statistics and their applications in engineering and computer science. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Students will learn statistical computing using the freely available R statistics software.
Credits (TN) :

4

Credits (US) :

2

UE :

UEF 310

Course Details :

CS 342
Algorithms & Complexity
Course Description
This course introduces students to the design and analysis of algorithms and the foundational principles of computational complexity. Topics include correctness of algorithms, asymptotic notation, time complexity of algorithms, and NP-completeness. Several algorithm design techniques are discussed including divide and conquer, dynamic programming, and greedy algorithms. Algorithms based on these techniques will be studied for solving a wide variety of problems such as sorting, searching, string processing, optimization, and graph problems (breadth-first search, depth-first search, minimum spanning trees and shortest path algorithms). Students will also explore the theoretical limits of computation through complexity classes and tractability
Credits (TN) :
4
Credits (US) :
2
UE :
UEF 320

Course Details :

CS 342
OO Software design & construction
Course Description
This course provides a comprehensive introduction to object-oriented design and programming in Java. Students will learn the principles of object-oriented design, including abstraction, encapsulation, inheritance, and polymorphism, and apply these principles in building robust, maintainable software using Java libraries and frameworks. The course will also initiate students to software design patterns and testing, to be covered in depth by later courses.
Credits (TN) :
7
Credits (US) :
3.
UE :
UEF 330

Course Details :

CS 411
Computer Networks
Course Description
This is an introductory course in computer networking. The goal is to introduce students to networks architectures and protocols, and to help them understand why they are designed the way they are and how they are likely to evolve in the future. In particular, the course will emphasize the principal performance and engineering tradeoffs in the design and implementation of modern computer networks. We will draw examples primarily from the Internet. Topics to be covered include congestion/flow/error control, routing, addressing, naming, multi-casting, switching, internetworking, and network security.
Credits (TN) :
5
Credits (US) :
2.5
UE :
UEF 340

Course Details :

CS 225
Business & Technical Communication
Course Description
This course introduces students with the skills of composing clear and professional technical documents, such as technical reports and memos, problem/issue and solution description memos, and product descriptions. To develop these skills, the course covers the principles of audience analysis, research, referencing, drafting and revision processes. Special focus is placed on technical documents in the computing field. During the course, students are introduced to the challenges of communicating in different workplace environments. Throughout the semester, students develop an in-depth understanding of different rhetorical situations. In particular, they develop and analyze the majority of the most common technical writing genres, such as emails, resumes, letters, memos, reports, proposals, etc
Credits (TN) :
4
Credits (US) :
2
UE :
UEF 310

Course Details :

CS 321
Principles of software engineering
Course Description
This course provides a comprehensive introduction to the principles, methodologies, and practices of software engineering, with an emphasis on ethical standards, and professional responsibility. Topics include, but are not limited to, software development lifecycle, software methodologies, requirements engineering, UML, software qualities. Through a series of progressive activities and case studies, students will not only gain a thorough understanding of the technical aspects of building high-quality software design model but also learn to work in teams and prioritize ethical practices in all stages of software engineering.
Credits (TN) :
6
Credits (US) :
3
UE :
UEO 310

Course Details :

CS 336
Data warehousing & management
Course Description
This course covers the fundamental principles and techniques of data warehousing and data management. Students will learn how data warehouses are designed, implemented, and maintained to support decision-making processes in organizations. Topics include data warehousing architecture, data modeling, ETL (Extract, Transform, Load) processes, and data governance. Hands-on assignments will involve creating and managing a data warehouse using modern tools.
Credits (TN) :

4

Credits (US) :

2

UE :
UEF 410

Course Details :

CS 425
Web application development
Course Description
This course introduces students to designing and developing modern web applications. Students will learn both frontend and backend development, integrating databases, web frameworks, and APIs. Emphasis is placed on responsive design and deploying web applications. The course introduces students to the architecture, system, and components of web applications that are instrumental to the success of many e-commerce websites and enterprise networks. Topics include client-server architecture, server-side technologies for handling client requests, session handling, user data validation, the MVC framework, advanced HTM and form elements, persistence and database access, and web services.
Credits (TN) :
5
Credits (US) :
2.5
UE :
UEF 420

Course Details :

CS 428
Software Testing & quality assurance
Course Description
Software Quality Assurance (SQA) is a critical activity integrated throughout the software development lifecycle. It involves processes, techniques, and practices aimed at ensuring that software systems, products, or services meet the functionality, reliability, and usability requirements of their target users. This course offers a comprehensive exploration of SQA principles, focusing on both theoretical foundations and practical applications. Students will learn to implement effective quality management strategies, apply assurance processes, and master testing methodologies within the broader framework of modern software engineering practices.
Credits (TN) :
5
Credits (US) :
2.5
UE :
UEF 430

Course Details :

CS 481
Artificial intelligence
Course Description
This is an introductory course to artificial intelligence that covers fundamental topics in AI, specifically search, reasoning and knowledge representation, and machine learning. The course provides students with a general overview of the artificial intelligence field with a focus on machine learning. It will cover the principal models and algorithms for supervised and unsupervised learning (classification, regression, clustering and dimensionality reduction). The course will initiate students to the basic ideas and intuition behind the main machine learning methods as well as a more formal understanding of how and when they work. Students will be introduced to the principal ethical challenges of applying AI in society.
Credits (TN) :
4
Credits (US) :
2
UE :
UEF 440

Course Details :

PHIL 222
Contemporary Issues in Data Ethics
Course Description
This course is an introduction to ethics in general and to contemporary ethical issues arising from the use of information technology in the modern information age. The course explores the moral, social, and ethical ramifications of the choices we make at the different stages of the data acquisition and use, starting from data collection and storage to the implications of certain learning and classification algorithms when applied to data about citizens and communities. Relying on case studies and class discussions, applied team projects, students will be introduced to the complexities of ethical decision-making, in general, and in data science applications. Topics include an overview of the different ethical theories (relativism, subjectivism, utilitarianism…) and the ethical challenges associated with modern data science and particularly with the gathering, preprocessing, modeling and sharing of data.
Credits (TN) :
6
Credits (US) :
3
UE :
UEF 410

Course Details :

CS 370
Human Computer Interaction
Course Description
This course introduces students to the design of effective human-computer interfaces using a human-centered approach. The course covers the principles of human-computer interaction and the design and evaluation of user interfaces. Topics include the characteristics of human information processing and cognition, and how these characteristics affect the design of user interfaces. Topics include techniques for designing, building, and evaluating computer interfaces with a human-centered approach, best practices and known design principles.
Credits (TN) :
6
Credits (US) :
3
UE :
UEO 410

Course Details :

CS 455
Cloud computing
Course Description
This course introduces students to the field of Cloud Computing, its enabling technologies, core concepts and main building blocks. Cloud computing services are being widely adopted to implement a utility-like provisioning of computing resources and services. Topics include, cloud infrastructures, virtualization, cloud storage, programming models, security, and service delivery models (SLAs). The course focusses on virtualization as a key cloud technique for offering software, computation, and storage services, covering examples from Xen and VMWare, and presenting real use cases such as Amazon EC2. Students will also learn about different cloud storage concepts including data distribution, durability, consistency and redundancy, distributed file systems, NoSQL databases and object storage. Students will also be introduced to the MapReduce programming model as well as message queues and stream processing. Students will work with Amazon Web Services and Microsoft Azure, use them to rent and provision compute resources and then program and deploy applications that run on these resources.
Credits (TN) :
5
Credits (US) :
2.5
UE :
UEF 510

Course Details :

CS 426
Mobile application development
Course Description
This course provides a comprehensive exploration of mobile application development, focusing on industry-compliant user interfaces, data management, and advanced mobile features. Students will learn to design and develop responsive, user-friendly applications that seamlessly pass data between screens, utilize local storage, and integrate location-based services for smarter, more interactive experiences.
The course emphasizes hands-on development, guiding students through the process of building and deploying fully functional apps on Android-compatible devices. By the end of the course, students will have the technical expertise to develop real-world mobile applications while adhering to industry best practices in UI/UX design, data handling, and mobile optimization.
Credits (TN) :
5
Credits (US) :
2.5
UE :
UEF 520

Course Details :

CS 412
Computer security
Course Description

This course provides an in-depth introduction to computer security theory and practice, equipping students with the knowledge to identify, analyze, and mitigate security threats. Students will explore key security concepts, including risks, vulnerabilities, and countermeasures, through real-world case studies and hands-on exercises. Topics covered include malware threats, access control mechanisms, password security and best practices, cryptography fundamentals, as well as operating system, network, and database security. By the end of the course, students will have a strong foundation in cybersecurity principles and the ability to implement effective security measures in modern computing environments.

Credits (TN) :
4
Credits (US) :
2
UE :
UEF 530

Course Details :

CS 423
Software architecture & design patterns
Course Description
This course introduces students to the design, implementation, and evolution phases of software development; Software design processes, methods, and notation; Implementation of designs; Evolution of designs and implementations; and Management of design activities. Topics include software architecture, architectural styles, and architectural representations; software design, design patterns, design representations; software architecture and design conception, analysis, and communication; architecture and design recovery / reverse engineering; architecture and design visualization / understanding; and cloud / grid computing architectures.
Credits (TN) :
4
Credits (US) :
2
UE :
UEF 540

Course Details :

Business & Entrepreneurship Elective
Course Description
Credits (TN) :
6
Credits (US) :
3
UE :
UET 510

Course Details :

MGMT 322
Project management
Course Description
This course provides an in-depth understanding of project management principles tailored for software engineering. Students will learn industry-standard methodologies, tools, and best practices for planning, executing, monitoring, and delivering software projects on time and within budget. Topics include agile and traditional project management frameworks, risk assessment, resource allocation, stakeholder communication, and quality assurance. The course emphasizes practical applications through case studies, group projects, and hands-on software tools.
By the end of the course, students will be equipped with critical thinking, leadership, and decision-making skills essential for managing software projects successfully.
Credits (TN) :
6
Credits (US) :
3
UE :
UEO 510

Course Details :

MGMT 321
Capstone project/Internship (Stage de fin d’études)
Course Description
Final Year Project & Report.
Credits (TN) :
30
Credits (US) :
15
UE :
UEF 610

Course Details :

MGMT 38
Mathematical foundations of data science
Course Description
This course is an introduction to key mathematical concepts underpinning machine learning and data science. The first part of this course covers basics of linear algebra and aims to bridge the gap into linear algebra problems, and how to apply these concepts to machine learning. Topics include solving systems of linear equations, application of common vector and matrix algebra, linear transformations, determinants and eigenvectors, application of linear algebra concepts to machine learning problems such as neural networks, dimensionality reduction and image compression. The second part dives into the basics of calculus including derivatives and optimization, gradients and gradient descent, optimization in neural networks and newton’s method. The third and last part covers the basic concepts of probability and statistics for data science applications. It provides the fundamental tools for probability calculus and probability distribution functions, for single and multiple variables, then shifts to the foundations of sampling and point estimation by reviewing concepts related to the law of large numbers, central limit theorem, statistical inference methods. This part is concluded by focusing on some key issues and central problems in Machine learning using tools and concepts presented in the previous topics of the course.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 1

Course Details :

CS 435
Big data technologies & applications
Course Description
This course explores the landscape of big data technologies and their real-world applications. Students will gain hands-on experience with big data platforms, tools, and techniques while exploring case studies from various domains. Topics covered include overview of big data management systems, distributed data storage, Hadoop, programming models for Big Data (MapReduce and RDD), Spark, column-based storage and analytics on big data, Big spatial data, document databases, machine learning on big data, and Big-data visualization
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 2

Course Details :

MGMT 431
Data management for data scientists
Course Description
This course aims to equip data scientists with the skills and knowledge to effectively manage data using both relational and non-relational database systems. Students will learn data modeling, database design, query processing and optimization, SQL, NoSQL databases, and practical data management techniques.
Credits (TN) :
6
Credits (US) :
3
UE :
UE 3

Course Details :

COM 425
Advanced technical communication
Course Description
This advanced English for Specific Purposes (ESP) course is designed to equip master’s students with essential business and technical writing skills relevant to their academic and professional careers. Participants will refine their ability to produce a range of technical documents, including reports, memos, emails, CVs, proposals, product descriptions, and business pitches. Learning will be reinforced through case studies, presentations, and real-world applications to ensure both theoretical understanding and practical expertise. Beyond writing proficiency, students will develop critical thinking and problem-solving skills, particularly in the context of technical and IT-related documentation. The course fosters both lower-order and higher-order thinking skills, encouraging students to leverage their prior knowledge and analytical abilities to reach the creative stage of Bloom’s Digital Taxonomy. By integrating receptive and productive language skills—writing, reading, listening, and speaking—alongside 21st-century competencies, this course provides a comprehensive framework for effective technical communication in professional and academic settings
Credits (TN) :
6
Credits (US) :
3.5
UE :
UE 4

Course Details :

COM 521
Software engineering for data scientists
Course Description
This course introduces students to the methods, approaches, and tools for the design of software systems, with emphasis on methods applied in large product development projects as well as modern trends in software engineering. Topics include development lifecycle models, such as agile development, requirements analysis and specification, the Unified Modeling Language (UML), design approaches, and testing. The course covers all technical phases of the software process including requirements, software architecture and design, improving the quality of code, formal approaches, issues in maintenance and operations. The course covers the advanced models of modern software development process.
Credits (TN) :
4
Credits (US) :
2
UE :
UEO 5

Course Details :

COM 483
Machine Learning
Course Description
This is an introductory course to machine learning. It provides students with an overview of several key concepts, techniques, and algorithms in machine learning. It will cover the principal models and algorithms for supervised and unsupervised learning (classification, regression, clustering and dimensionality reduction). The course will initiate students to the basic ideas and intuition behind main machine learning methods as well as a more formal understanding of how and when they work
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 6

Course Details :

CS 535
Scalable big data processing
Course Description
This course is a second course in Big Data management. It reviews the fundamental concepts and algorithms of big data analytics and focuses on practical and advanced aspects of big data management. Topics include programming models, search techniques, data mining principles, parallel data mining techniques, as well as data mining for unstructured data such as social network analysis and text mining. A further aim of the course is to introduce students to practical platforms and tools for Big Data Analytics such as Hadoop, Hive, HCatalog, and Pig (actual platforms subject to update)
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 7

Course Details :

COM 451
Distributed systems
Course Description
This is an introductory course to distributed systems covering the major principles and techniques for creating correct, high-performance, scalable, and reliable distributed systems. The course provides students with an understanding of the core principles and techniques behind the design of real-world distributed systems, especially those employed to handle naming, communication, coordination, concurrency control, replication, and security. Topics include process distribution and communication, data storage and access, scheduling, concurrency, resource sharing, naming, distributed programming models, web services, virtualization, fault-tolerance, and security. Through projects, students will gain practical experience in designing, implementing, and debugging real distributed systems.
Credits (TN) :
6
Credits (US) :
3
UE :
UE 8

Course Details :

COM 435
Effective professional presentations
Course Description
This course is designed to equip graduate students with the essential skills to deliver compelling and impactful presentations in both academic and professional settings. Through a combination of theoretical concepts, practical exercises, and real-world case studies, students will develop confidence and proficiency to communicate their ideas effectively. This course is endorsed by artificial intelligence and digital tools to meet the learning objectives of the 21st century skills, sustainable development, and quality education.
Credits (TN) :
6
Credits (US) :
3
UE :
UE 9

Course Details :

COM 470
Data visualization for data scientists

Course Description

This course is designed to provide students with a comprehensive understanding of data storytelling and visualization using Tableau. Through a hands-on approach, students will learn to communicate effectively with data, understand the importance of visualization, and master the functionalities of Tableau to create impactful visualizations and dashboards. Students are also introduced to data visualization techniques using PowerBI and Python libraries.
Credits (TN) :
4
Credits (US) :
2
UE :
UEO 10

Course Details :

CS 585
Deep learning and neural networks
Course Description
This course offers a comprehensive introduction to deep learning, a specialized area within machine learning that leverages neural networks with multiple layers to tackle complex problems. Students will explore the fundamental concepts, advanced techniques, and cutting-edge architectures integral to deep learning. The curriculum encompasses essential models and algorithms for both supervised and unsupervised learning, with a focus on convolutional neural networks (CNNs). Through this course, students will develop a robust understanding of deep learning techniques, their practical applications, and gain insights into optimizing models. By the end of the course, students will be equipped with the knowledge to effectively implement and assess deep learning solutions.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 11

Course Details :

CS 555
Cloud computing for data scientists
Course Description
This course introduces students to the field of Cloud Computing, its enabling technologies, core concepts and main building blocks. Cloud computing services are being widely adopted to implement a utility-like provisioning of computing resources and services. Topics include, cloud infrastructures, virtualization, cloud storage, programming models, security, and service delivery models (SLAs). The course focusses on virtualization as a key cloud technique for offering software, computation, and storage services, covering examples from Xen and VMWare, and presenting real use cases such as Amazon EC2. Students will also learn about different cloud storage concepts including data distribution, durability, consistency and redundancy, distributed file systems, NoSQL databases and object storage. Students will also be introduced to the MapReduce programming model as well as message queues and stream processing. Students will work with Amazon Web Services, use it to rent and provision compute resources and then program and deploy applications that run on these resources. The course also presents a complete material to prepare for the highly valuable AWS professional certification “cloud practitioner”.
Credits (TN) :
7
Credits (US) :
3.5
UE :
UE 12

Course Details :

CS 581
Generative AI and large language models
Course Description
This course provides an in-depth exploration of Generative AI and Large Language Models (LLMs), focusing on their architectures, training methodologies, and applications. Students will learn the underlying principles of transformers, self-supervised learning, and prompt engineering, while also engaging with hands-on implementation and ethical considerations. Throughout the course, students will work on real-world applications of LLMs, including text generation, conversational agents, creative AI, and domain-specific fine-tuning. By the end of the semester, students will have a solid understanding of how to develop, evaluate, and deploy generative AI models in practical settings.
Credits (TN) :
6
Credits (US) :
3
UE :
UE 13

Course Details :

PHIL 222
Contemporary issues in Data ethics
Course Description
This course is an introduction to ethics in general and to contemporary ethical issues arising from the use of information technology in the modern information age. The course explores the moral, social, and ethical ramifications of the choices we make at the different stages of the data acquisition and use, starting from data collection and storage to the implications of certain learning and classification algorithms when applied to data about citizens and communities. Relying on case studies and class discussions, applied team projects, students will be introduced to the complexities of ethical decision-making, in general, and in data science applications. Topics include an overview of the different ethical theories (relativism, subjectivism, utilitarianism…) and the ethical challenges associated with modern data science and particularly with the gathering, preprocessing, modeling and sharing of data.
Credits (TN) :
6
Credits (US) :
3
UE :
UET 14

Course Details :

CS 586
AI for emerging applications
Course Description
This course introduces students to the real-world application of AI techniques, including machine learning, natural language processing, and deep learning, in emerging domains. Students will tackle three key areas of AI application through hands-on projects, addressing specific challenges and deploying AI solutions. While students will be introduced via the various case studies to different applications of AI, each group of students will select a single project to focus on and complete during the term. This is an integrative course preparing students to choose a capstone project in the following and final semester in their Master’s program. The course also allows students to get additional practice in the techniques and tools they covered in prior and concurrent courses, in particular TensorFlow, PyTorch (for deep learning), NetworkX, Gephi (for social network analysis), and Cloud Platforms (AWS, Google Cloud) for deployment
Credits (TN) :
4
Credits (US) :
2
UE :
UEO 15

Course Details :

CS 586
Master Thesis/Project (Mémoire de Stage de fin d’études)
Course Description
Master’s Project & Thesis.
Credits (TN) :
30
Credits (US) :
15
UE :
UEF 16
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