# MSc in Data Science & Artificial Intelligence. Canonical HTML: https://dsti.school/msc-in-data-science-and-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 Science • Artificial Intelligence • Machine Learning DSTI’s flagship postgraduate route for students who want to model complex problems, understand machine learning deeply, and connect mathematical AI with real data, software and engineering environments. This is the scientific route for students who want to build, evaluate and explain models — not only use AI tools, but understand the statistical, mathematical and computational logic behind them. Apply now Book a meeting Compare MSc routes Download brochure Model the real world Statistics, machine learning, optimisation and AI for complex problems. Deep AI foundations Neural networks, deep learning, high-dimensional data and advanced modelling. Production-aware science MLOps, cloud, data engineering and software foundations for deployable AI. Programme centre of gravity Statistical modelling, machine learning and AI systems with engineering awareness. For students who want the scientific depth behind data science, and the technical culture to apply it in real contexts. 120 ECTS Master’s level programme • RNCP level 7 840h Total programme volume 3IA-labelled programme Labelled by the 3IA programme of Université Côte d’Azur. 01 — Programme identity ## Data science is not only prediction. It is rigorous modelling. The MSc in Data Science & Artificial Intelligence is the route for students who want the strongest modelling, machine-learning and AI centre of gravity in DSTI’s postgraduate portfolio. DSTI positioning ## From mathematical depth to AI practice. The programme combines statistics, optimisation, machine learning, deep learning, advanced modelling and data engineering foundations, so graduates can reason about models and understand the systems that bring them into use. Statistics ### Understand uncertainty Learn the statistical reasoning behind inference, modelling, validation and decision-making under uncertainty. Machine Learning ### Build and evaluate models Work with supervised, unsupervised and advanced modelling approaches, with practical implementation. Deep Learning ### Move into modern AI Study neural networks, deep learning, high-dimensional data and applications such as computer vision and NLP. Engineering ### Connect science to systems Cloud, SQL, MLOps, software engineering and big data foundations help models move beyond notebooks. 02 — Who this programme is for ## For students who want the modelling and AI centre of gravity. This MSc is mathematically and technically demanding. It is best suited to students who want to understand models deeply and use them responsibly in real data contexts. Good fit ### You want to become technically credible in modelling, machine learning and AI. - You like mathematics, statistics, modelling, experimentation and model validation. - You want advanced machine learning, deep learning and AI foundations. - You want enough data engineering and MLOps culture to understand how models are used in practice. - You are aiming for data scientist, machine-learning engineer, AI specialist or modelling-oriented roles. Less ideal if ### Your main goal is business analytics or infrastructure engineering. - If your main interest is decision support, reporting and analytics implementation, compare with MSc in Data Analytics with AI. - If your main interest is pipelines, cloud platforms and production data systems, compare with MSc in Data Engineering for 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. 840h 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. 60h 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 Science & Artificial Intelligence 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 ### Statistics, AI and engineering A transparent course-by-course structure covering modelling, optimisation, deep learning, MLOps, big data and governance. 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 helps students from varied backgrounds reinforce the mathematics, programming, AI awareness and IT foundations needed for a serious MSc in Data Science and AI. 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. Core Data Science & AI 190h • 24 ECTS ### Mathematics, statistics, optimisation and neural networks This block establishes the scientific and mathematical foundations of the programme: statistical analysis, optimisation, time series, SAS and neural networks. 25h 3 ECTS #### Mathematics for Data Science Calculus, linear algebra and complex numbers required for optimisation and data science. 25h 3 ECTS #### Foundations of Statistical Analysis and Machine Learning — Part 1 Descriptive statistics, probability theory and applications using R. 40h 4 ECTS #### Foundations of Statistical Analysis and Machine Learning — Part 2 Tests, estimators, confidence intervals, inference, ANOVA, PCA, linear regression and applications using R. 25h 3 ECTS #### Time-Series Analysis Temporal data analysis with mathematical foundations and applications in R for forecasting and prediction. 25h 3 ECTS #### SAS Base Preparation for SAS Base certification, covering SAS Base programming and applications in SAS STATS. 25h 4 ECTS #### Continuous Optimisation Critical points, optimisation of multivariable functions, gradient methods and constraint-based optimisation using Lagrange multipliers. 25h 4 ECTS #### Artificial Neural Networks Neural network layers, weights, biases, hyperparameters, optimisation algorithms and TensorFlow implementation. Core Data Engineering 250h • 24 ECTS ### Software, cloud, SQL, MLOps and big data foundations Data scientists need engineering literacy. This block connects modelling with the software, cloud, databases, MLOps and big data environments that make AI deployable. 25h 2 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. 50h 4 ECTS #### MLOps by Adaltas DevOps, GitOps, DataOps, MLOps, unit testing with Spark, CI/CD, artifact deployment, Databricks and MLFlow. 25h 3 ECTS #### Data Wrangling with SQL Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and Microsoft SQL Server. 50h 4 ECTS #### Cloud Computing — Amazon AWS AWS cloud services and preparation for AWS Certified Solutions Architect – Associate. 50h 4 ECTS #### Big Data Ecosystem by Adaltas HDFS, scheduling, resource management, ETL, scalable enterprise service bus, Spark, data exploration and visualisation. Advanced Data Science & AI 215h • 32 ECTS ### Advanced modelling, deep learning and complex systems This block is the advanced modelling core: high-dimensional statistics, survival analysis, inverse problems, graph and document databases, deep learning, ABM and semantic web. 35h 4 ECTS #### Advanced Statistical Analysis and Machine Learning Multiple linear regression, CART, random forests, feature selection, model comparison and practical applications in R. 25h 3 ECTS #### Statistical Analysis of Massive and High-Dimensional Data Analysis of large datasets including open data and social networks, with modern statistical tools and practical implementation in R. 25h 3 ECTS #### Survival Analysis Parametric, non-parametric and semi-parametric survival analysis techniques. 25h 4 ECTS #### Inverse Problems & Data Assimilation Variational and sequential data assimilation for initial condition identification and parameter estimation, with Python applications. 25h 4 ECTS #### Graph Databases — NoSQL Part 1 Preparation for Neo4j Certified Professional and graph-based problem modelling with practical Neo4j implementations. 5h 2 ECTS #### Document Databases — NoSQL Part 2 MongoDB collections, documents, advanced queries, aggregations, architecture and practical applications. 25h 4 ECTS #### Deep Learning Deep learning models in Python, with practical applications in computer vision and natural language processing. 25h 4 ECTS #### Agent-Based Modelling Complex problem modelling with ABM, comparisons with statistical, Markov and system dynamics approaches, and ABM validation. 25h 4 ECTS #### Semantic Web Technologies RDF, SPARQL and standards for representing, querying and using knowledge on the web. Operational Methodologies 50h • 4 ECTS ### Governance, law and project context Data science and AI are used within organisations and legal environments. This block addresses data regulation, ethics and project methodology. 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. Support & Professional Experience 60h 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. 60h #### Support Sessions Reviewing course topics, answering questions, re-explaining harder concepts 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 for data science 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. Data Science students are also prepared for AWS and SAS credentials. 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. 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 AWS ### AWS Certified Solutions Architect – Associate Course context: Cloud Computing - Amazon AWS Certification website SAS ### Base Programming Specialist Course context: SAS Base Programming 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 Databricks #### Databricks Certified Associate Developer for Apache Spark MLOps Official page Databricks #### Databricks Certified Data Engineer Associate MLOps Official page Databricks #### Databricks Certified Data Engineer Professional MLOps 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 IBM #### IBM Data Science Professional Certificate Data science foundations Official page Neo4j #### Neo4j Graph Data Science Certification Graph data science Official page N NVIDIA #### NVIDIA-Certified Associate Generative AI LLMs Generative AI and LLMs Official page Google #### Certified Generative AI Leader Generative AI leadership Official page Dataiku #### ML Practitioner Certificate (Dataiku) Machine learning practice Official page Dataiku #### MLOps Practitioner Certificate (Dataiku) MLOps practice Official page Dataiku #### Developer Certificate (Dataiku) Dataiku development Official page scikit-learn #### Scikit-learn Certification (all levels accepted) Machine learning with Python Official page Google #### Google Cloud Platform Associate Cloud Engineer (ACE) Cloud engineering Official page Denodo #### Denodo AI SDK Certified Developer Associate certification AI SDK development 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 science becomes real when models are used responsibly. 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 modelling and AI 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 modelling steps ### Analyse, model and validate 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. Progression path ### Move from models to deployed value 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. 08 — Admissions ## Selective admission for a modelling-oriented MSc route. The MSc in Data Science & Artificial Intelligence requires mathematical readiness, English proficiency and the ability to progress through demanding technical and statistical coursework. Eligibility ### Bachelor degree or equivalent 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. Entry exam ### Used when helpful 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. 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): Continuous Optimisation; Artificial Neural Networks. - Continuous Optimisation - Artificial Neural Networks - 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 Science & 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