Master en Science des Données
⸺The Master in Data Science & AI, fully taught in English according to the North American model, trains the data scientists, AI engineers and researchers of tomorrow, for both academia and industry.
Digital transformation is producing ever larger volumes of data, most of it now “born digital”. Turning this data into knowledge requires solid foundations in mathematics and statistics, scalable big data and cloud technologies, and modern machine learning, deep learning and generative AI. The program answers the shortage of such experts in Tunisia, the region and worldwide, and prepares graduates to identify patterns, predict trends and support decision makers in every sector.
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Applicants must hold a Licence or Bachelor degree in computer science, computer engineering, electrical engineering, mathematics or a related field. Recommended background: programming (preferably Python), and fundamentals of linear algebra, probability and statistics, and databases. Applications are submitted online through the Application Process page. For the full admission policy, tuition fees and scholarships, see the Admission menu.
The objective of the Master in Data Science & AI is to train data scientists, AI engineers, researchers and data analytics developers able to design, build and deploy data-driven and AI solutions, in academia and in industry.
The specific objectives are to provide our graduates with:
- A solid foundation in the mathematical and statistical principles of data science and machine learning.
- Mastery of data management, big data technologies and scalable data processing, including distributed systems and cloud platforms.
- The ability to design, train and evaluate machine learning and deep learning models, including neural networks.
- Hands-on knowledge of generative AI and large language models, and of AI for emerging applications.
- Skills in data visualization and software engineering practices for building reliable data and AI products.
- An in-depth understanding of the ethical, legal and societal challenges, risks and implications of data and AI solutions.
- Strong technical communication and professional presentation skills.
Specific potential careers include:
- Spécialiste des données.
- Machine learning engineer / AI engineer.
- Generative AI and LLM engineer (NLP, intelligent assistants, AI applications).
- Analyste de données / Consultant en analyse d'entreprise.
- Data engineer / Big data engineer.
- MLOps and cloud data engineer.
- Research & development engineer in AI, data mining and knowledge extraction.
- Designer of specialized software solutions for the processing and analysis of large amounts of data.
- AI and data ethics consultant in information-intensive industries.
- Project manager in data and AI projects.
A few years after successfully completing the Master, graduates are expected to be:
- Employed in industry and demonstrating career advancement through leadership responsibility, significant technical achievement or other recognition.
- Continuing their education towards a PhD or other professional certification in data science and AI, or leading their own technology venture.
- Developing data science and AI solutions (data collection, analysis, machine learning and deep learning) and applying them to real problems.
- Working as data scientists, AI/ML engineers, data analysts, data engineers, research engineers, or architects and managers of data and AI systems.
- Demonstrating an in-depth understanding of the challenges faced by industry and society in data analysis and responsible AI.
| Semester | Related course(s) | Recommended certification | Provider / platform |
| Semester 1 | CS 435 Big data technologies & applications (also supports CS 431) | AWS Academy Data Engineering | Amazon Web Services, via AWS Academy |
| Semester 2 | CS 483 Machine Learning | IBM Machine Learning Professional Certificate | IBM, via Coursera for Campus |
| Semester 3 | CS 585 Deep learning and neural networks (also supports CS 581 and CS 586) | Deep Learning Specialization | DeepLearning.AI, via Coursera for Campus |
| Code du cours | Titre du cours | UE | ||
| CS 482 | Mathematical foundations of data science | UE 1 | ||
| CS 435 | Big data technologies & applications | UE 2 | ||
| CS 431 | Data management for data scientists | UE 3 | ||
| COM 425 | Advanced technical communication | UET 4 | ||
| CS 521 | Software engineering for data scientists | UEO 5 |
| Code du cours | Titre du cours | UE | ||
| CS 483 | Machine Learning | UE 6 | ||
| CS 535 | Scalable big data processing | UE 7 | ||
| CS 451 | Distributed systems | UE 8 | ||
| COM 435 | Effective professional presentations | UET 9 | ||
| CS 470 | Data visualization for data scientists | UEO 10 |
| Code du cours | Titre du cours | UE | ||
| CS 585 | Deep learning and neural networks | UE 11 | ||
| CS 555 | Cloud computing for data scientists | UE 12 | ||
| CS 581 | Generative AI and large language models | UE 13 | ||
| PHIL 222 | Contemporary issues in Data ethics | UET 14 | ||
| CS 586 | AI for emerging applications | UEO 15 |
| Code du cours | Titre du cours | UE | ||
| ISS 521 | Master Thesis/Project (Mémoire de Stage de fin d’études) | UEF 16 |
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