Bachelor’s Degree in Big Data and Data Analytics

Turn raw data into clear analysis and useful decisions.

Learn to collect, prepare, analyse and communicate data. The curriculum combines programming, SQL and statistics with data pipelines, dashboards, machine learning, deep learning and distributed processing. It connects analytical work with the engineering practices needed to make data and models useful in real applications.

Accredited for the 2026–2027 academic year.

Discuss my study options

Is this pathway right for you?

A good fit if you enjoy patterns, quantitative reasoning and explaining what evidence can—and cannot—tell you. Choose this pathway for analytics and data systems rather than a general management qualification.

What you will learn to do

Clean, explore and assess the quality of datasets with programming and database tools.
Build analytical models, visualisations and dashboards that answer a defined question.
Evaluate regression, classification and deep-learning approaches.
Develop data pipelines and explore cloud, distributed processing and model deployment.

Illustrative application: clean a dataset, build a dashboard, compare predictive models and explain the limits of the results.

From coursework to practice

Semester 5 covers automated data-science pipelines, streaming, financial data applications and advanced AI topics. Semester 6 is a 14-week PFE worth 30 credits.

Career pathways

Relevant entry-level directions include:

Junior data analyst
Junior BI developer
Junior data engineer
Data-science assistant
MLOps assistant
Junior Big Data developer

Career progression depends on your skills, experience and employer requirements.

Further study

Possible postgraduate directions include data science, AI, business intelligence, data engineering and cloud computing, subject to each programme’s admissions requirements.

Compare your options

Big Data and Data Analytics combines analysis with data engineering. Software focuses on applications; Networks and Cybersecurity focuses on infrastructure and protection.

Your curriculum, semester by semester

Semester 1 · 30 credits

Algorithmique I — 3 credits
Atelier Programmation en C I — 3 credits
Algèbre linéaire — 2 credits
Logique et combinatoire — 2 credits
Atelier Introduction aux systèmes d’exploitation — 3 credits
Atelier Réseaux informatiques fondamentaux — 3 credits
Modélisation des données — 2 credits
Atelier Langage SQL — 3 credits
Atelier Technologies du Web — 2 credits
Atelier Programmation Web côté client — 3 credits
Digital Skills 1 — 1 credit
Study Skills 1 — 2 credits
Français Pro 1 — 1 credit

Semester 2 · 30 credits

Algorithmique II — 3 credits
Atelier Programmation en C II — 3 credits
Probabilités et statistiques — 2 credits
Analyse numérique — 2 credits
Atelier Python et Données — 4 credits
Programmation orientée objet — 2 credits
Atelier Programmation orientée objet — 3 credits
Conception orientée objet — 2 credits
Atelier Réseaux — 2 credits
Atelier Administration des systèmes — 3 credits
Anglais 1 — 1 credit
Study Skills 2 — 2 credits
Français Pro 2 — 1 credit

Semester 3 · 30 credits

Statistiques inférentielles — 2 credits
Atelier Modélisation probabiliste — 3 credits
Préparation des données — 2 credits
Atelier Méthodes exploratoires — 3 credits
Introduction au Data Mining — 2 credits
Atelier Requêtes avancées — 3 credits
Modélisation analytique — 2 credits
Pipelines de données — 2 credits
Atelier Qualité des données — 3 credits
Anglais 2 — 1 credit
Soft Skills : Projet professionnel personnalisé — 2 credits
Français Pro 3 — 1 credit
Python pour les données — 1 credit
Atelier Bibliothèques scientifiques — 3 credits

Semester 4 · 30 credits

Atelier Validation et évaluation — 3 credits
Régression et classification — 2 credits
Conception de visualisations — 2 credits
Atelier Tableaux de bord — 3 credits
Fondements du Deep Learning — 3 credits
Atelier Deep Learning — 3 credits
Conteneurisation & orchestration — 3 credits
Atelier Services cloud pour la data — 3 credits
Entrepreneuriat 1 — 1 credit
Anglais 3 — 1 credit
Soft Skills : Développement de compétences personnelles et professionnelles — 2 credits
Représentations du texte — 2 credits
Atelier Modèles séquentiels — 2 credits

Semester 5 · 30 credits

CI/CD pour la data science — 2 credits
Atelier Automatisation des pipelines & tests ML — 3 credits
Fondements Big Data et systèmes distribués — 2 credits
Atelier Traitement par flux — 3 credits
Atelier Modélisation & risques financiers — 2 credits
Données et pipelines financiers — 2 credits
Architectures DL avancées et applications IA — 4 credits
Atelier Transfer Learning, Vision et Segmentation — 3 credits
Entrepreneuriat 2 — 1 credit
Soft Skills : Préparation à la vie professionnelle et personal branding — 2 credits
Digital Skills 2 — 1 credit
Certification Cloud Data — 2 credits
Atelier Préparation Certification — 3 credits

Semester 6 · 30 credits

Projet de fin d’études (PFE) – 14 semaines — 30 credits

Plan your admission

New applicants: Tell admissions your qualification, results and intended intake. Ask the team to confirm eligibility and any admission assessment.

Transfer applicants: Prepare transcripts and course descriptions for an individual credit review. The approved decision determines your remaining study requirements.

Fees and scholarships: Request a written breakdown of tuition, registration charges, payment dates and any scholarship decision.

Language and schedule: Confirm the language of instruction, weekly timetable and examination arrangements before registration.

The curriculum includes cloud-data certification preparation. External certification awards, examinations and fees are separate matters to confirm with admissions.

Discuss my study options

Email APBS admissions | Transfer guidance | Fees and scholarships | Student and alumni stories