# MSc in Data Engineering for Artificial Intelligence. Canonical HTML: https://dsti.school/msc-in-data-engineering-for-ai This Markdown copy is generated from the same DSTI static-site build as the canonical HTML page. It is intended for machine readability and concise retrieval. MSc • Data Engineering • Artificial Intelligence • Cloud & DevOps A postgraduate route for students who want to build the technical foundations behind modern AI: data architectures, storage systems, pipelines, cloud infrastructure and production-ready engineering practices. This is the engineering route for students who want to make AI possible at scale: not just experimenting with models, but designing, operating and securing the data systems that feed them. Apply now Book a meeting Compare MSc routes Download brochure Build AI infrastructure Design the storage, pipelines and platforms that make AI projects possible. Cloud & DevOps culture AWS, Azure, CI/CD, containers, orchestration and operational discipline. From data to models Connect data engineering with machine learning and deep learning applications. Programme centre of gravity Data architectures, pipelines and platforms that power AI systems. For students who want to understand the engineering layer between raw data and artificial intelligence. 120 ECTS Master’s level programme • RNCP level 7 800h Total programme volume 3IA-labelled programme Labelled by the 3IA programme of Université Côte d’Azur. 01 — Programme identity ## AI needs models, but it also needs engineering infrastructure. Data Engineering for AI is the MSc route for students who want to build, automate and operate the technical systems that make artificial intelligence usable in real organisations. DSTI positioning ## From pipelines to AI-ready systems. Many AI projects fail before modelling begins because data is fragmented, unreliable or impossible to operationalise. This programme focuses on the architecture, software, cloud, pipeline and DevOps skills needed to make data usable for AI. Architecture ### Design data systems Learn to structure storage, computation and data flows for real technical environments. Pipelines ### Move and transform data Build pipelines between systems, using modern data engineering formats and platforms. Cloud ### Operate scalable platforms Understand AWS, Azure and cloud-based infrastructure for modern data and AI work. AI ### Support machine learning Connect engineering practice with the mathematical and practical foundations of AI. 02 — Who this programme is for ## For students who want to build the systems behind AI. The MSc in Data Engineering for AI is a technical route. It is ideal for students who like systems, software, cloud, databases, pipelines and operational reliability. Good fit ### You want to become the person who makes data and AI systems work in production. - You like databases, cloud computing, platforms, automation and distributed systems. - You want strong software engineering and DevOps foundations. - You want to connect data pipelines with machine learning and AI applications. - You are aiming for data engineering, cloud, platform or AI engineering roles. Less ideal if ### Your main goal is business reporting or model research. - If your main interest is decision support, dashboards and analytics implementation, compare with MSc in Data Analytics with AI. - If your main interest is advanced modelling, statistics and deep AI methods, compare with MSc in Data Science & AI. - If your main interest is systems protection and risk, compare with MSc in Cyber Security. 03 — Programme structure ## The programme structure, at a glance. The structure combines technical warm-up, specialist coursework, support sessions and integrated professional experience. 800h Total taught programme volume across warm-up, specialist coursework and support sessions. 75h DSTI Warm Up to reinforce mathematics, programming, AI awareness, IT foundations and systems practice. 55h Support sessions for review, questions, difficult concepts and examination preparation. 30 ECTS Professional experience through internship, employment or validated professional activity. 04 — Curriculum ## Transparent programme content, course by course. DSTI does not hide behind vague course labels. The MSc in Data Engineering for AI is shown below through its teaching blocks, hours, ECTS and course content. Warm Up • 75h ### Technical starting point A preparatory bridge for students from varied academic backgrounds before entering the full MSc core. Specialist coursework ### Engineering, AI and operations A transparent course-by-course structure covering software engineering, cloud, data management, DevOps, AI and security. Professional experience • 30 ECTS ### Workplace application A validated professional experience where students apply the programme in a relevant working environment. Warm Up 75h • 6 ECTS ### Common technical starting point The Warm Up reinforces mathematics, programming, data management, AI awareness and IT foundations before students enter the full MSc core. 10h #### Fundamental Mathematics Core mathematical preparation for quantitative and technical coursework. 20h #### Data Structure and Machine Learning using Python & R Programming, data structures and first machine-learning workflows using Python and R. 5h #### Data Management Introduction to how data is organised, stored, queried and prepared for later work. 5h #### AI Awareness Initial orientation on artificial intelligence concepts, uses and limits. 5h #### Computer Architecture Essential concepts about how computers execute, store and process information. 5h #### Networking Basic networking knowledge for students who will work with connected systems and platforms. 10h #### Computer Systems Labs Practical systems work to consolidate the IT foundation of the programme. 10h #### Clean IT Responsible and sustainable digital practice. 5h #### Excel Basics Baseline spreadsheet skills for students who need to consolidate fundamentals. Software Engineering & IT 200h • 25 ECTS ### Cloud, software engineering and machine-learning implementation This block builds the computer-engineering foundation behind data and AI systems: cloud platforms, software design, semantic web, web engineering and machine-learning implementation. 50h 4 ECTS #### Cloud Computing — Amazon AWS AWS cloud services and preparation for AWS Certified Solutions Architect – Associate. 25h 3 ECTS #### Cloud Computing — Microsoft Azure Comparative overview with AWS, focusing on Azure services relevant to data lakes and data pipelines. 25h 4 ECTS #### Semantic Web Technologies RDF, SPARQL and standards for representing, querying and using knowledge on the web. 25h 3 ECTS #### Software Engineering — Part 1 Procedural programming in Rust, memory representation, ownership, borrowing, data structures, testing and reliable code. 25h 3 ECTS #### Software Engineering — Part 2 Object-oriented programming in C#, SOLID, UML, MVC, design patterns, services and Entity Framework. 25h 4 ECTS #### Python Machine Learning Labs Data structures, data cleaning, feature engineering and machine-learning modelling with Python libraries. 25h 4 ECTS #### Web Engineering HTML, CSS, JavaScript, front-end basics, MVC programming with ASP.NET and API frameworks. Data Management 180h • 28 ECTS ### Storage, warehousing, NoSQL, big data and pipelines This is the centre of gravity of the programme: data storage, data warehouses, ETL, graph and document databases, big data ecosystems and pipeline technologies. 25h 3 ECTS #### Data Wrangling with SQL Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and Microsoft SQL Server. 25h 3 ECTS #### Data Warehousing & ETL Design and implementation of data warehouses and ETL processes in stand-alone and cluster deployments. 25h 4 ECTS #### Graph Databases — NoSQL Part 1 Graph-based problem modelling and practical implementation in Neo4j graph databases. 10h 2 ECTS #### Document Databases — NoSQL Part 2 MongoDB collections, documents, advanced queries, aggregations and data architecture. 50h 4 ECTS #### Big Data Ecosystem by Adaltas HDFS, scheduling, resource management, workflow and ETL, scalable enterprise service bus, Spark and data exploration. 25h 4 ECTS #### Data Pipeline — Part 1 XML data flow, DTD and schemas, XSL transformations and JSON data formats. 25h 3 ECTS #### Data Pipeline — Part 2 Apache Spark, Kafka, modern data platforms, Lambda and Kappa architectures, anything-as-code and CI/CD practices. Operational Methodologies 175h • 18 ECTS ### DevOps, security, information systems and governance Engineering data systems also requires project methods, legal awareness, DevOps practice, cyber security foundations and information-system design. 25h 2 ECTS #### Data Laws & Regulations — Philosophies, Geopolitics & Ethics Data privacy and security principles, EU and US regulation, and differences between common law and code law. 25h 2 ECTS #### IT Project Management: Traditional and Agile Approaches Project management lifecycle and best practices for traditional and agile approaches. 25h 4 ECTS #### CRM Data Management Preparation for Microsoft Power Platform Functional Consultant and CRM data management software. 50h 4 ECTS #### DevOps by Adaltas Nagios, Consul, Docker, Ansible, GitHub, Jenkins, Kubernetes and continuous integration practices. 25h 4 ECTS #### Fundamentals of Cyber Security Practices System security design patterns, infrastructure security, data encryption and code safety. 25h 2 ECTS #### Analysis & Design of Information Systems Principles and methodologies for designing and analysing information systems. Data Science 125h • 18 ECTS ### Mathematics, statistics, machine learning and deep learning The programme connects data engineering with the science and applications of AI, so graduates understand not only the pipelines, but also the models they support. 25h 3 ECTS #### Mathematics for Data Science Calculus, linear algebra and complex numbers needed for optimisation and data science. 25h 3 ECTS #### Foundations of Statistical Analysis & Machine Learning — Part 1 Descriptive statistics, probability theory and applications using R. 25h 4 ECTS #### Big Data Processing with R Importing, manipulating, transforming, visualising, exploring and modelling large datasets in R. 25h 4 ECTS #### Artificial Neural Networks Neural network layers, weights, biases, hyperparameters, optimisation and TensorFlow implementation. 25h 4 ECTS #### Deep Learning Deep learning models in Python, with practical applications in computer vision and natural language processing. Support & Professional Experience 55h support • 30 ECTS experience ### Consolidation and workplace application Support sessions help students consolidate difficult concepts, while professional experience validates the ability to apply the programme in a relevant working environment. 55h #### Support Sessions Reviewing course topics, answering student questions, re-explaining harder notions and preparing for examinations. 4 to 6 months 30 ECTS #### Integrated Professional Experience Internship, employment or contracting experience validated through DSTI’s standard evaluation procedures. 05 — Professional certification ## Certification that supports engineering credibility. MSc Data students must validate the Neo4j certification. A second approved certification is highly recommended and grants the “with Honours” distinction to graduates. MSc Data rule ### Neo4j is mandatory. The Data Engineering route foregrounds cloud and CRM systems. For Data Engineering for AI, DSTI prepares programme certifications through CRM Data Management and Amazon AWS cloud computing. Additional certifications may be accepted only when Faculty and Direction of Studies validate their level, scope and relevance to the student’s pathway. Mandatory Neo4j certification for every MSc Data student. With Honours A second approved certification grants the “with Honours” graduate distinction. Controlled alternatives Alternative certifications are accepted only after Faculty and Direction of Studies validation. Neo4j ### Neo4j certification Course context: Graph Databases NoSQL Certification website Microsoft ### Exam PL-200: Microsoft Power Platform Functional Consultant Course context: CRM Data Management Certification website AWS ### AWS Certified Solutions Architect – Associate Course context: Cloud Computing - Amazon AWS Certification website Validated certification pathway ### Accepted alternative certifications This list is not a free-for-all. Each alternative certification must be relevant to the student’s study route and career objectives, and remains subject to Faculty and Direction of Studies validation. MongoDB #### All Mongo certification NoSQL - MongoDB Official page C++ Institute #### CPA – C++ Certified Associate Programmer Certification Software Engineering 2 Official page Oracle #### Oracle Database SQL Certified Associate Datawarehousing & ETL Official page Oracle #### MySQL 8.0 Database Developer Oracle Certified Professional Datawarehousing & ETL Official page Google #### Google Data Analytics Professional Certificate Big Data Processing with R Official page AWS #### AWS Certified Cloud Practitioner Cloud Computing - Amazon AWS Official page AWS #### AWS Certified Machine Learning - Specialty Cloud Computing - Amazon AWS Official page Microsoft #### Microsoft Certified: Power Platform Developer Associate CRM Data Management Official page Terraform #### Terraform Associate Data Pipeline 2 Official page Microsoft #### Microsoft Certified: DevOps Engineer Expert DevOps Official page Google #### Certified Professional Data Engineer DevOps Official page Microsoft #### Microsoft Certified: Azure Solutions Architect Expert Microsoft Azure Official page Microsoft #### Microsoft Certified: Azure Data Scientist Associate Microsoft Azure Official page Microsoft #### Microsoft Certified: Azure Administrator Associate Microsoft Azure Official page Microsoft #### Microsoft Certified: Fabric Data Engineer Associate Microsoft Azure Official page Elastic #### Elastic Certified Engineer Exam Search and data engineering Official page Elastic #### Elastic Certified Observability Engineer Exam Observability engineering Official page Microsoft #### Microsoft Certified: Fabric Analytics Engineer Associate Microsoft Fabric analytics Official page dbt #### dbt Analytics Engineering Certification Exam Analytics engineering Official page Fortinet #### Fortinet Certified Associate Cybersecurity Cybersecurity fundamentals Official page Denodo #### Denodo Platform 9 Certified Developer Associate Data virtualisation Official page Databricks #### Databricks Certified Data Engineer Associate Data engineering platforms Official page scikit-learn #### Scikit-learn Certification (all levels accepted) Machine learning with Python Official page Cisco #### Preparation for the Cisco Certified Network Associate (CCNA) Certification Networking fundamentals Official page 06 — Where and how you study ## One DSTI programme, several ways to join the class. DSTI was built around connected teaching. Students can study on campus, through Live Streamed access, or through the online route when this mode fits their situation. French Riviera / Sophia-Antipolis Study in Europe’s first science and technology park, near Nice and Antibes. Paris Campus Study in central Paris, close to companies, transport and student life. Live Streamed Attend the same classes live from another location, with the same academic expectations. Online asynchronous Access the programme through DSTI’s online route when this mode best fits your situation. 07 — Careers ## Data engineering becomes real when systems are operated. The programme includes integrated professional experience. The route depends on where the student is based and what makes professional sense. Primary route ## Do a relevant internship where your engineering project makes sense. DSTI can support professional experience in France or abroad, subject to academic validation and programme rules. The work must be relevant to the learning outcomes. Local internship ### Your country or professional market For many Live Streamed or international students, the best route is a relevant internship in their local job market. Structured alternative ### CRCC Asia option Where relevant, DSTI may guide students towards a structured international internship option through CRCC Asia. Visit CRCC Asia Career support ### CV, profiles and positioning Students receive guidance on CVs, public profiles, applications and professional positioning. Early engineering steps ### Build and operate data pipelines Typical first roles include Junior Data Engineer, ETL / Data Pipeline Developer, Analytics Engineer or Cloud Data Support Engineer, depending on the student’s technical base and project exposure. Progression path ### Move towards platform responsibility With experience, graduates can progress towards Data Engineer, Cloud Data Platform Engineer or DataOps / MLOps Engineer roles. Architecture responsibilities normally come later, after real system delivery. 08 — Admissions ## Selective admission for a technical MSc route. The MSc in Data Engineering for AI is a technical route. Mathematics, English and IT readiness matter, and the admissions process should confirm that the applicant can progress seriously. Eligibility ### Bachelor degree or equivalent Applicants should hold a recognised Bachelor degree or equivalent in Computer Engineering, Information Systems, Computer Science or a closely related computing discipline. Entry exam ### Used when helpful Applicants from less technical backgrounds may be asked to take DSTI’s online entry exam in mathematics and IT. English ### B2 minimum Courses are delivered in English. IELTS 6.0, Duolingo 110 or equivalent evidence may be required. IT requirements ### Windows PC laptop Students should have a Windows PC laptop with at least 16GB RAM, able to run the latest Windows version. Direct entry into Year 2 + Applicants who have completed, or are finishing, either a first year of a Master’s-level programme in the same area or a four-year Bachelor in the same area may request direct entry into Year 2. Direction of Studies decides on admission after a specific academic review. Required common exams: Applied Mathematics; Foundations of Statistical Analysis — Part 1; Data Wrangling with SQL. Additional programme exam(s): Analysis & Design of Information Systems; Cloud Computing — Amazon AWS — DSTI Internal Exam; Software Engineering with C. - Analysis & Design of Information Systems - Cloud Computing — Amazon AWS — DSTI Internal Exam - Software Engineering with C - Exams are taken online on DSTI Learn with computer-vision enabled proctoring and Safe Exam Browser, within 30 days upon application. - A €50 supplemental examination fee is required and credited towards tuition fees upon admission. Direction of Studies looks at applicants achieving DSTI’s minimum pass grade of 60%. Read the direct-entry guidance Autumn 2026 ### October entry - International students: 26th June 2026 - EU & Live Streamed students: 31st July 2026 - Induction: 2nd October 2026 - Classes start: 5th October 2026 Spring 2027 ### March entry - International & Live Streamed students: 22nd January 2027 - EU students: 12th March 2027 - Induction: 25th March 2027 - Classes start: 26th March 2027 ## Is Data Engineering for AI the right route for you? If you are hesitating between Data Analytics with AI, Data Engineering for AI and Data Science & AI, the key question is your centre of gravity: decision and implementation, platforms and pipelines, or modelling and AI systems. Book a meeting Apply now Compare MSc routes On this page