DSTI Applied MSc in Data Engineering for Artificial Intelligence master’s level programme addresses the growing market demand for professionals skilled in organising data storage and pipelines, as well as implementing AI algorithms. As organisations move beyond the initial ‘all data science’ trend, they recognise the necessity of efficiently managing diverse data types and volumes, from small tabular datasets to large unstructured data.
Data Engineering is a specialised branch of computer engineering that encompasses all these activities our programme equips you with:
To effectively implement AI, one must understand the applied mathematics that underpin artificial intelligence – a core focus of AI Engineering. DSTI’s Applied MSc in Data Engineering for AI uniquely combines data engineering skills with the science and applications of machine learning, providing a comprehensive pathway to becoming an Artificial Intelligence Engineer.
Apply now to accelerate your career and embark on the path to becoming an Artificial Intelligence Engineer.
All DSTI MSc programmes – Key Dates for the 2025 Entries
Application deadlines for:
Induction Days:
Classes start dates:
Application deadlines for:
Induction Days:
Classes start dates:
Please note that Self-Pace Online Course (SPOC) applicants are not subject to any particular application timeline
As a front-runner in data and AI in France, DSTI offers a Bachelor programme at RNCP level 6. Our MSc programmes hold a RNCP level 7 accreditation. DSTI is recognised under the 3IA Cote d’Azur Label for extensive AI content and proudly possesses Qualiopi RNQ certification, affirming the quality of our processes. Our Qualiopi certificate can be downloaded by clicking on this link.
The following are the major objectives of the Applied MSc in Data Engineering for Artificial Intelligence programme:
Acquire detailed knowledge and skills for creating and monitoring IT and Big Data infrastructures.
Explore DevOps and establish continuous integration frameworks to enhance your software creation and roll-out process.
Learn key programming languages and libraries for applied machine and deep learning, honing abilities to construct and roll out complex models for practical applications.
Enhance your abilities to create and manage highly distributed data and computation clusters like Hadoop or Spark, enabling efficient and effective large-scale data handling.
DSTI Applied MSc in Data Engineering for Artificial Intelligence programme grants 120 ECTS. It encompasses 800 hours of actual classes, equivalent to 90 ECTS, including a 75-hour DSTI Warm Up for technical proficiency and an additional 45 hours of support sessions. Lastly, the professional experience phase (internships or apprenticeships), valued at 30 ECTS, offers practical data engineering experience.
Any course and/or block of courses/skills (teaching unit) validated according to the knowledge assessment procedures automatically constitutes partial validation, with the associated ECTS credits.
24-34
Years of average age range
77%
of International Students
7+
Hands-on projects
3
International Certifications preparation
DSTI offers the Applied MSc in Data Engineering for AI in two modes: Undergraduate & Postgraduate Education and Executive & Continuing Lifelong Education.
Undergraduate & Postgraduate Education is designed for students under 30 transitioning from school or university, preparing them to become proficient data professionals. Choose between two options: Full-Time or Part-Time (Apprenticeship).
For beginners in Data Engineering, we suggest the 2-year Full-time mode with options for two data-related internships, the second being mandatory.
The apprenticeship mode combines part-time work and study, open only to EU students or those with a long-term visa in France. Read details before applying.
For professionals typically 30 or older, Executive & Continuing Lifelong Education balances career growth and work commitments. It’s perfect for those with relevant experience or tech education, allowing flexible completion of the Applied MSc in Data Engineering for AI on-campus or online.
SPOC is ideal for students balancing studies with regular jobs. Coursework, completed between 15 – 36 months through recorded lectures, can be supplemented with live online sessions if available. The course duration is flexible to the student’s needs.
DSTI also provides a ‘Part-time Sandwich’ or ‘Contrat de Professionnalisation’. This option is perfect for those aged 30 and above, French speakers, and individuals who are EU/EEA citizens or long-stay visa holders in France.
The Software Engineering & IT module in the Applied MSc in Data Engineering for AI programme covers courses including Amazon AWS, Microsoft Azure, Web and Software Engineering, Python Machine Learning Labs, and Semantic Web Technologies for developing data science skills.
The Software Engineering & IT module in the Applied MSc in Data Engineering for AI programme includes Amazon AWS, Microsoft Azure, web and software engineering, Python Machine Learning Labs, and Semantic Web Technologies for developing data science skills.
Learn to use the different cloud services on the AWS platform and prepare for the AWS Certified Solutions Architect – Associate certification.
This course provides a comparative overview with Amazon AWS and focuses on Microsoft Azure services relevant to data lakes and data pipelines.
A course that provides an introduction to web technology, covering RDF and SPARQL for representing and querying web-rich data, as well as representing and using knowledge on the web with standardised frameworks.
This course covers the fundamentals of algorithmics and data structures using classical design and programming, with a focus on practical applications in the C language.
A course that covers the fundamentals of algorithmics and data structures using object-oriented programming, with a focus on practical applications in the C++ and Python programming languages.
This course provides an overview of data structures, data cleaning and preparation techniques, feature engineering and machine learning modelling with Python libraries.
This course provides a comprehensive understanding of web development basics through HTML, CSS and JavaScript for front-end development. It also includes an introduction to MVC programming with ASP.NET for back-end and an overview of API framework.
The Data Management module covers SQL Data Wrangling, Data Warehousing, ETL, Graph and Document NoSQL Databases, Big Data Ecosystems and Data Pipelines.
A course that covers the fundamentals of relational databases, advanced SQL queries, stored procedures, triggers dynamic SQL and their applications with Microsoft SQL Server.
A course that covers the design and implementation of a data warehouse, building an Extract, Transform, Load process and their applications in stand-alone and cluster deployments.
A course that provides preparation for the Neo4j professional certification and covers graph-based problem modelling with practical implementations in Neo4j graph databases.
A course that covers MongoDB database technology including, collections and documents, advanced MongoDB queries and aggregations, the MongoDB data architecture as well as practical applications.
A course that covers HDFS, scheduling & resource management, workflow management & ETL, dataflow management, scalable enterprise serial bus, real-time processing with SPARK and data exploration & visualisation.
A course that covers XML data flow, DTD and schemas, XSL transformations and JSON data formats for building efficient and scalable data pipelines.
This course covers the fundamentals of data engineering technologies like Apache Spark, Kafka, and modern data platform components. It also introduces the Lambda and Kappa architectures and the concept of “anything as code,” as well as modern CI/CD practices.
A module centred around project management methodologies and ethical and social questions related to data use.
The course covers the principles and frameworks of data privacy and security, in EU & US regulations as well as the differences between common law and code law.
This course covers the project management lifecycle, as well as best pratices for implementing and working with different approaches like the Agile methodology.
A course that covers the preparation for the Microsoft Power Platform Functional Consultant (PL-200) certification, and introduces in Customer Relationship Management (CRM) data management software.
A course that focuses on the various tools and technologies involved in DevOps, including Nagios, Consul, Docker, Ansible, GitHub, and Continuous Integration with Jenkins and Kubernetes.
A course that covers system security design patterns, infrastructure security, data at rest and in-transit encryption and code safety to provide a comprehensive understanding of how to protect computer systems and networks from cyber threats.
This course covers the principles and methodologies behind designing and analysing information systems.
After this module, students will gain a comprehensive understanding of the mathematical and statistical foundations of data science, as well as practical skills in big data processing and machine learning.
This course covers the basic notions of applied mathematics required to study optimisation for data science: calculus, linear algebra and complex numbers.
A course that introduces the fundamentals of descriptive statistics, probability theory, and their applications using the R programming language for data analysis.
This course teaches how to import, manipulate, transform, visualise, explore, and model large datasets in R, with a focus on selecting the best data structures for optimal performance.
This course delves into the fundamental concepts of neural network’s layers, weights, biases, and hyperparameters, as well as optimisation algorithms. Students will learn the applications of neural networks in classification and regression problems and how to implement them in Python using TensorFlow.
This course introduces students to deep learning models using Python libraries with a focus on practical applications in computer vision and natural language processing.
Support sessions are dedicated to reviewing course topics in depth, answering student questions, re-explaining harder notions, and preparing for examinations.
After completing the coursework, students will be required to demonstrate a professional experience between 4 and 6 months. This could take the form of an internship, employment, or contracting, as long as the standard DSTI evaluation procedures are met. This practical experience enables students to apply their learning in a working environment.
Our curriculum enables students to prepare for the following certifications
One of which is necessary to graduate (Neo4J Certified Professional). One other certificate is highly recommended for employability but is not mandatory for graduation.
Below are some of the professors for the Applied MSc in Data Engineering for AI programme.
Vice Dean
Hanna Abi Akl is a renowned scientist, author, and researcher in language, logic, and artificial intelligence, with expertise in language structure, understanding and generation, as well as symbolic and graph-based knowledge retrieval methods in AI. He currently serves as an NLP Researcher and Professor at Data ScienceTech Institute.
Professor
Dr. Catherine Faron is a respected full professor at Université Côte d’Azur. She holds the position of vice-head of Wimmics, a collaborative research team between the I3S laboratory and the Inria centre.
Professor, Co-president of the Scientific Board
Fabien Gandon, a Senior Researcher at INRIA and I3S Sophia Antipolis in France, specializes in Semantic Web, Ontologies, Knowledge Engineering and Modelling, Corporate Memories, and other areas in the field of informatics and computer science.
AWS Academy, from which DSTI was one of the first French institutions to be accepted, provides higher education institutions with a ready-to-teach cloud computing curriculum that prepares students to pursue industry-recognised certifications and in-demand cloud jobs. AWS Academy curriculum helps educators stay at the forefront of AWS Cloud innovation so that they can equip students with the skills they need to get hired in one of the fastest-growing industries.
Again, as one of the first French institution members of the Microsoft Learn for Educators (MSLE), DSTI faculty can easily integrate AI Skills, technical skills, and industry-trusted and verified credentials into their curriculum to set students up for real-world success.
Azure for Education offers students access to an impressive library of fully licensed Microsoft software, developer tools, and cloud resources, including a yearly $100 voucher. These resources are vital for learning and completing AI-related projects, leveraging the Microsoft cloud ecosystem.
O’Reilly is a comprehensive platform providing over 60,000 books, 30,000 hours of video, live events, and interactive labs on topics like cloud computing, software development, AI, and machine learning. It offers students extensive learning resources to deepen their knowledge in AI and related fields.
Students gain access to our partner Adaltas cloud-based cluster, which leverages Hadoop and Spark for processing large-scale data sets. This enterprise-grade cluster provides 24/7 availability, allowing students to experience real-world big data environments.
All students enrolled in the programme receive access to a SAS license. This software suite is essential for advanced analytics, business intelligence, and data management tasks, providing students with tools relevant to AI-based decision-making.
Through DSTI Learn, based on the latest Moodle LMS core, students can access their study materials, including notifications, course schedules, exams, live sessions, recorded lectures, and assignment submissions. This platform ensures students remain connected with their coursework anytime, anywhere.
DSTI offers a support system through Zendesk, allowing students to seek help for academic, career, or administrative queries. Students can also revisit past responses, ensuring continuous support throughout their studies.
DSTI students are provided with lifelong access to an alumni email account, along with Microsoft Windows and Office 365 licenses, enabling them to stay connected and use essential software for professional and personal use.
The Applied MSc in Data Engineering for AI at DSTI equips students for a flourishing future in the AI industry. A commendable 95% of our students obtain internship offers within a span of 6 months. Moreover, 2 out of 3 students successfully receive CDI offers through our apprenticeships and contrat pro schemes.
95%
of students get an internship offer within 6 months
1,000€ +
average monthly stipend
75%
of students find internships in Europe
49k€
average starting salary
1,600€
average monthly stipend for Apprenticeship
1,950€
average monthly stipend for Contrat Pro
2/3
students receive permanent job offers post first experience.
50% +
students sign their contracts through DSTI
The admission procedure at DSTI School of Engineering is a selective and competitive, yet inclusive endeavour that provides deserving candidates a fair chance. This outlined admission process applies to all study modes.
Please note that DSTI does not offer parallel admission for direct entry in second year of its Applied MSc programmes.
To qualify for DSTI’s Applied MSc programmes, applicants must satisfy these conditions:
Applicants should have studied Mathematics at high school level or possess an equivalent qualification.
Candidates must have completed a 3 or 4-year Bachelor degree or equivalent from a recognised university.
DSTI provides three ways for prospective students to demonstrate their academic credentials. Students may only submit one type of academic record from the three options provided. However, submitting evidence of more than one qualification listed below will enhance your chances of admission.
Option 1: Minimum Grades + Bachelor Degree Certificate
For consideration into the Applied MSc programme, candidates must attain at least the following grades or their equivalents: USA – GPA 2.0; Germany – 3.5; France – 12; UK – 2:2 (2nd Class Lower Division); India – CGPA 6.5 or Upper second class; China – 67%.
Option 2: standard admission test + Bachelor Degree Certificate
To uphold application quality, we value scores from standardised tests. For the GRE, aim for a minimum of 155 in the quantitative section and an average total score close to 300. For the GMAT, target a minimum score of 42, with an average total score approaching 600.
Option 3: Online DSTI Entry Exam + Bachelor Degree Certificate
If the above criteria are unattainable, consider taking the online DSTI Entry Exam from home. All that’s needed is a computer and stable internet access. The exam comprises two sections: Mathematics and IT.
Since all courses are taught in English, students must have a B2 level of proficiency in English. DSTI will assess English proficiency during the Admission Interview.
To boost an application, students may submit their IELTS or TOEFL scores.
Students at DSTI should have a Windows PC laptop, not Apple Mac, with these minimum specs:
At least Intel Core i5 quad-core or Intel i7 dual-core (or AMD equivalent).
8GB as the absolute minimum, but 16GB highly recommended.
Minimum 512GB, 1TB recommended. SSD required.
It’s a personel choice of investment. If to be done, NVIDIA GPU.
Your PC must be able to run the main latest Windows version (currently: Windows 11).
DSTI will provide a Windows Education (eq. to Enterprise edition) key when classes start.
Don’t purchase MS Office 365: DSTI will provide a full product license when classes start.
DSTI School of Engineering admission process is a comprehensive exercise that offers an opportunity to all deserving candidates.
To start your application, browse our various Applied MSc programmes to find your perfect match. Schedule an online meeting with our team for guidance and to check each programme’s tuition fees.
The application is done online, and we assess your suitability. You’ll need to upload standard documents: ID, CV, and a cover letter.
After evaluating your application, DSTI will ask you to submit your academic records and may ask you to complete an entry exam.
Academic Records
All students must submit their academic records plus either of the below options can also be completed or requested:
Option 1: standard admission test + bachelor degree certificate
To uphold application quality, we value scores from standardised tests. For the GRE, aim for a minimum of 155 in the quantitative section
and higher than 280 for an average total. For the GMAT, target a minimum score of 42, or over 560.
This can be submitted in addition to your academic records to boost your application.
Option 2: Online DSTI Entry Exam
We may ask you to complete the DSTI Entry Exam in addition to submitting your academic records. The exam can be completed from home. All that’s needed is a computer and stable internet access. The exam comprises two sections: Mathematics and IT.
If your application advances, we’ll invite you for a 20-minute admission interview to confirm your interest, course suitability and English fluency.
Upon acceptance, you’ll receive an official admission decision via email.
Please refer to our detailed admission process for more information.
DSTI – School of Engineering
Private Higher Education Institution
As a front-runner in data and AI in France, DSTI School of Engineering offers an Applied Bachelor at RNCP 6. Our Applied MSc hold RNCP 7 accreditation. Further, DSTI is recognised under the 3IA Cote d’Azur Label for extensive AI content and proudly possesses Qualiopi RNQ certification, affirming quality of processes (download Qualiopi certificate).
DSTI School of Engineering has formed strategic partnerships and affiliations with a number of key organisations, including the likes of AWS, SAS, Microsoft, Arts et Métiers, and 3IA Côte d’Azur. These partnerships are vital because they help keep our syllabus current and our resources updated. With these partners, we are better equipped to support our students as they advance their careers in data.
950 Route des Colles
Les Templiers
06410 Biot (Sophia Antipolis)
Alpes-Maritimes, France
4 Rue de la Collégiale
75005 Paris
Île-de-France, France
+33 (0) 489 412 944
© 2025 All Rights Reserved.
At DSTI, we provide one-on-one online meetings with prospective students. Here we answer all their questions regarding our Bachelors and MSc programmes.
At DSTI, we organise online group meetings where we share valuable information about our selection of Bachelors and MSc programmes in data and AI.
DSTI organises online group meetings to provide information about our range of Bachelor and MSc programmes in data and AI.
Every Wednesday from 2PM to 6PM CEST, DSTI’s Paris Campus hosts an open day for all, no appointment necessary. Inquiries regarding admission, courses or other related topics are welcomed. We are delighted to provide answers to your questions.
| Fees are valid for the Autumn 24 and Spring 25 intake. | Applied MSc in Data Analytics | Applied MSc in Data Engineering for Artificial Intelligence | Applied MSc in Data Science & Artificial Intelligence | Applied MSc in Cyber Security |
|---|---|---|---|---|
| Total Tuition Fees | 18,700€ | 18,700€ | 18,700€ | 25,000€ |
| Yearly Tuition Fees | 9,350€ | 9,350€ | 9,350€ | 12,500€ |
*No tuition fees for the students in apprenticeship mode.
| Fees are valid for the Autumn 24 and Spring 25 intake. | Executive MSc in Artificial Intelligence |
|---|---|
| Total Tuition Fees | 18,700€ |
Want a career in data and AI? Download the DSTI’ Applied MSc in Data Science and AI Curriculum to find out how!
