DSTI Applied MSc in Data Science & Artificial Intelligence is our flagship master’s-level programme, forged from years of continuous improvement and close alignment with the global job market expectations. It is renowned for its unique combination of mathematical rigour, advanced machine learning techniques, and practical computer engineering & IT applications. You will gain deep expertise in developing, optimising, and deploying AI-driven models to solve complex problems across all sectors of the global economy.
Throughout the programme, you will build the essentials skills to become a forward-thinking Data Scientist:
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 Applied Data Science & AI programme:
Our programme's goal is to boost your mathematical skills and their application to resolve intricate Data Science and AI challenges.
Our course centres on giving students a profound grasp of the core AI algorithms, encompassing machine learning, deep learning and natural language processing.
Our programme aids students in utilising their scientific skills by teaching them to analyse, design, implement, and monitor IT and Big Data frameworks.
Our programme imparts knowledge of IT project management and the legal implications of data handling, including privacy laws. It also introduces ethical considerations of mining big data.
DSTI Applied MSc in Data Science & Artificial Intelligence is an all-encompassing programme with 120 ECTS. It comprises two parts: firstly, 840 hours of coursework equivalent to 90 ECTS, beginning with a 75-hour DSTI Warm-Up to sharpen necessary skills, and 60 hours of support sessions. Secondly, internships or apprenticeships, carrying 30 ECTS credits, provide excellent practical experience in data science.
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.
24-33
Years of average age range
80%
of International Students
5+
Hands-on projects
3
International Certifications Preparation
DSTI offers the Applied MSc in Data Science & 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 Science, 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 Science & 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 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.
DSTI School of Engineering provides warm-up courses for the Applied MSc in Data Science & AI, ensuring all students, regardless of their background, start with an equal understanding and are able to reinforce their current knowledge to be fully prepared to start the programme. The warm up covers the following modules:
The Core Data Science & AI module in our Applied MSc programme dives into key topics like Applied Mathematics for data science, Foundations of Statistical Analysis, Time-Series Analysis, Continuous Optimisation, SAS “The SAS Ecosystem DSTI Chair”, and Artificial Neural Networks.
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.
A course that builds on the concepts covered in Part 1 and focuses on topics such as tests, estimators, confidence intervals, inference, ANOVA, PCA, simple linear regression, and their applications using R.
Learn to analyse temporal data with mathematical foundations and applications in R, for forecasting and predictions.
This course provides comprehensive preparation for SAS BASE Certification, covering SAS Base programming and its application in SAS STATS.
The course covers critical points, optimisation of multiple variable functions, gradient methods, and constraint-based optimisation using Lagrange multipliers.
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 module provides an in-depth look at Amazon AWS Cloud Computing, Software Engineering basics, Python Machine Learning Labs, Big Data Ecosystems, Data Wrangling with SQL and MLOps. It dives into software engineering concepts, machine learning fundamentals, relational database technologies, and MLOps and Big Data tools for robust data solutions.
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.
A course that introduces students to DevOps, GitOps, DataOps, and MLOps, unit testing with Spark Data Engineering, CI/CD, artifact deployment to registries such as Docker, JAR, and notebooks, GitOps and MLOps with Apache Liminal, Cloud and MLOps, and the Databricks platform and MLFlow for developing scalable and robust data solutions.
A course that covers the fundamentals of relational databases, advanced SQL queries, stored procedures, triggers dynamic SQL and their applications with Microsoft SQL Server.
Learn to use the different cloud services on the AWS platform and prepare for the AWS Certified Solutions Architect – Associate certification.
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.
These courses encompass advanced techniques in statistical analysis, machine learning, deep learning, and agent-based modelling. They offer a thorough grasp of data science concepts and their practical applications.
This course covers multiple linear regression, CART and Random Forests, feature selection and engineering, models comparison and competition, with a focus on practical applications in R.
This course prepares students to analyse large datasets including open data and social networks. It reviews conventional statistical methods and their application, alongside modern statistical tools, with practical implementation in R.
A course that provides a comprehensive study of survival analysis techniques using parametric, nonparametric, and semiparametric methods.
A course that covers variational and sequential data assimilation techniques for identification of initial conditions and parameter estimation with a practical application in Python.
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.
This course introduces students to deep learning models using Python libraries with a focus on practical applications in computer vision and natural language processing.
This course prepares students to solve complex problems using Agent-Based Modelling (ABM), comparisons with statistical, Markov and system dynamics approaches, and ABM validation for trustability.
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.
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 practices for implementing and working with different approaches like the Agile methodology.
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.
Following are some of the professors who teach Applied MSc in Data Science and AI.
Academic Member, Professor
Jacques Blum is a Professor Emeritus at the University of Nice and has previously held positions as a professor at the University Joseph Fourier Grenoble and a researcher at CNRS, and he is an Agrégé in Mathematics from the Ecole normale supérieure.
Professor
Julien Jacques is a current Professor in Statistics at Université Lumière Lyon 2, with previous lecturing experience at Université de Lille, and a PhD in Statistics from Université Grenoble Alpes.
Professor
Dr. Georgiy Bobashev, an RTI Senior Fellow, specializes in predictive modeling for health research. With 20+ years of experience, he applies artificial intelligence, machine learning, and biostatistics to diverse areas like substance use, HIV, cancer, and public policy.
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 Science & AI offers brilliant career opportunities. Almost all graduates secure an internship within six months in Europe, with monthly remuneration/compensation sitting around 1,300€.
98%
of students get an internship offer within 6 months
1,300€ +
average monthly compensation
91%
of students find internships in Europe
45k€
average starting salary
1,600€
average monthly salary for Apprenticeship
1,950€
average monthly salary for Contrat Pro
2/3
students receive CDI offers.
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!
