The structure combines technical warm-up, specialist coursework, support sessions and integrated professional experience.
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.
A preparatory bridge for students from varied academic backgrounds before entering the full MSc core.
A transparent course-by-course structure covering software engineering, cloud, data management, DevOps, AI and security.
A validated professional experience where students apply the programme in a relevant working environment.
The Warm Up reinforces mathematics, programming, data management, AI awareness and IT foundations before students enter the full MSc core.
Core mathematical preparation for quantitative and technical coursework.
Programming, data structures and first machine-learning workflows using Python and R.
Introduction to how data is organised, stored, queried and prepared for later work.
Initial orientation on artificial intelligence concepts, uses and limits.
Essential concepts about how computers execute, store and process information.
Basic networking knowledge for students who will work with connected systems and platforms.
Practical systems work to consolidate the IT foundation of the programme.
Responsible and sustainable digital practice.
Baseline spreadsheet skills for students who need to consolidate fundamentals.
This block builds the computer-engineering foundation behind data and AI systems: cloud platforms, software design, semantic web, web engineering and machine-learning implementation.
AWS cloud services and preparation for AWS Certified Solutions Architect – Associate.
Comparative overview with AWS, focusing on Azure services relevant to data lakes and data pipelines.
RDF, SPARQL and standards for representing, querying and using knowledge on the web.
Procedural programming in Rust, memory representation, ownership, borrowing, data structures, testing and reliable code.
Object-oriented programming in C#, SOLID, UML, MVC, design patterns, services and Entity Framework.
Data structures, data cleaning, feature engineering and machine-learning modelling with Python libraries.
HTML, CSS, JavaScript, front-end basics, MVC programming with ASP.NET and API frameworks.
This is the centre of gravity of the programme: data storage, data warehouses, ETL, graph and document databases, big data ecosystems and pipeline technologies.
Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and Microsoft SQL Server.
Design and implementation of data warehouses and ETL processes in stand-alone and cluster deployments.
Graph-based problem modelling and practical implementation in Neo4j graph databases.
MongoDB collections, documents, advanced queries, aggregations and data architecture.
HDFS, scheduling, resource management, workflow and ETL, scalable enterprise service bus, Spark and data exploration.
XML data flow, DTD and schemas, XSL transformations and JSON data formats.
Apache Spark, Kafka, modern data platforms, Lambda and Kappa architectures, anything-as-code and CI/CD practices.
Engineering data systems also requires project methods, legal awareness, DevOps practice, cyber security foundations and information-system design.
Data privacy and security principles, EU and US regulation, and differences between common law and code law.
Project management lifecycle and best practices for traditional and agile approaches.
Preparation for Microsoft Power Platform Functional Consultant and CRM data management software.
Nagios, Consul, Docker, Ansible, GitHub, Jenkins, Kubernetes and continuous integration practices.
System security design patterns, infrastructure security, data encryption and code safety.
Principles and methodologies for designing and analysing information systems.
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.
Calculus, linear algebra and complex numbers needed for optimisation and data science.
Descriptive statistics, probability theory and applications using R.
Importing, manipulating, transforming, visualising, exploring and modelling large datasets in R.
Neural network layers, weights, biases, hyperparameters, optimisation and TensorFlow implementation.
Deep learning models in Python, with practical applications in computer vision and natural language processing.
Support sessions help students consolidate difficult concepts, while professional experience validates the ability to apply the programme in a relevant working environment.
Reviewing course topics, answering student questions, re-explaining harder notions and preparing for examinations.
Internship, employment or contracting experience validated through DSTI’s standard evaluation procedures.
MSc Data students must validate the Neo4j certification. A second approved certification is highly recommended and grants the “with Honours” distinction to graduates.
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.
Course context: CRM Data Management
Certification websiteCourse context: Cloud Computing - Amazon AWS
Certification websiteThis 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
C++ Institute
OracleOracle
GoogleAWSAWSMicrosoft
TerraformMicrosoftGoogleMicrosoftMicrosoftMicrosoftMicrosoft
ElasticElasticMicrosoft
dbt
Fortinet
Denodo
Databricks
scikit-learn
CiscoDSTI 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.
Online asynchronous
Access the programme through DSTI’s online route when this mode best fits your situation.
The programme includes integrated professional experience. The route depends on where the student is based and what makes professional 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.
For many Live Streamed or international students, the best route is a relevant internship in their local job market.
Structured alternative
Where relevant, DSTI may guide students towards a structured international internship option through CRCC Asia.
Visit CRCC AsiaStudents receive guidance on CVs, public profiles, applications and professional positioning.
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.
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.
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.
Applicants should hold a recognised Bachelor degree or equivalent in Computer Engineering, Information Systems, Computer Science or a closely related computing discipline.
Applicants from less technical backgrounds may be asked to take DSTI’s online entry exam in mathematics and IT.
Courses are delivered in English. IELTS 6.0, Duolingo 110 or equivalent evidence may be required.
Students should have a Windows PC laptop with at least 16GB RAM, able to run the latest Windows version.
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.
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.
