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 Science & Artificial Intelligence 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 modelling, optimisation, deep learning, MLOps, big data and governance.
A validated professional experience where students apply the programme in a relevant working environment.
The Warm Up helps students from varied backgrounds reinforce the mathematics, programming, AI awareness and IT foundations needed for a serious MSc in Data Science and AI.
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 establishes the scientific and mathematical foundations of the programme: statistical analysis, optimisation, time series, SAS and neural networks.
Calculus, linear algebra and complex numbers required for optimisation and data science.
Descriptive statistics, probability theory and applications using R.
Tests, estimators, confidence intervals, inference, ANOVA, PCA, linear regression and applications using R.
Temporal data analysis with mathematical foundations and applications in R for forecasting and prediction.
Preparation for SAS Base certification, covering SAS Base programming and applications in SAS STATS.
Critical points, optimisation of multivariable functions, gradient methods and constraint-based optimisation using Lagrange multipliers.
Neural network layers, weights, biases, hyperparameters, optimisation algorithms and TensorFlow implementation.
Data scientists need engineering literacy. This block connects modelling with the software, cloud, databases, MLOps and big data environments that make AI deployable.
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.
DevOps, GitOps, DataOps, MLOps, unit testing with Spark, CI/CD, artifact deployment, Databricks and MLFlow.
Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and Microsoft SQL Server.
AWS cloud services and preparation for AWS Certified Solutions Architect – Associate.
HDFS, scheduling, resource management, ETL, scalable enterprise service bus, Spark, data exploration and visualisation.
This block is the advanced modelling core: high-dimensional statistics, survival analysis, inverse problems, graph and document databases, deep learning, ABM and semantic web.
Multiple linear regression, CART, random forests, feature selection, model comparison and practical applications in R.
Analysis of large datasets including open data and social networks, with modern statistical tools and practical implementation in R.
Parametric, non-parametric and semi-parametric survival analysis techniques.
Variational and sequential data assimilation for initial condition identification and parameter estimation, with Python applications.
Preparation for Neo4j Certified Professional and graph-based problem modelling with practical Neo4j implementations.
MongoDB collections, documents, advanced queries, aggregations, architecture and practical applications.
Deep learning models in Python, with practical applications in computer vision and natural language processing.
Complex problem modelling with ABM, comparisons with statistical, Markov and system dynamics approaches, and ABM validation.
RDF, SPARQL and standards for representing, querying and using knowledge on the web.
Data science and AI are used within organisations and legal environments. This block addresses data regulation, ethics and project methodology.
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.
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 questions, re-explaining harder concepts 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 Science & AI, DSTI foregrounds certifications prepared through Amazon AWS cloud computing and SAS Base Programming. Other recognised certifications may be accepted only after Faculty and Direction of Studies validate their level and relevance to the student’s career path.
Course 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
DatabricksDatabricksDatabricksAWSAWS
IBMNeo4j
Google
DataikuDataikuDataiku
scikit-learnGoogle
DenodoDSTI 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 Scientist, Machine Learning Analyst, Applied Data Scientist or AI / ML Intern, depending on the student’s mathematics, coding and project portfolio.
With experience, graduates can progress towards Data Scientist, Machine Learning Engineer, MLOps Engineer or Applied AI Specialist roles. Responsible deployment and measurable value matter more than job-title inflation.
The MSc in Data Science & Artificial Intelligence requires mathematical readiness, English proficiency and the ability to progress through demanding technical and statistical coursework.
Applicants should hold a recognised 3 or 4-year Bachelor degree or equivalent, in a field where applied mathematics has been studied, such as mathematics, physics, engineering, computer science or economics.
Applicants may be asked to take DSTI’s online entry exam in mathematics and IT, especially when admissions or direction of studies need further evidence of readiness.
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): Continuous Optimisation; Artificial Neural Networks.
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.
