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 Analytics with 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 analysis, modelling, data systems, visualisation 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 technical foundations needed to start the programme seriously.
Core mathematics needed to approach optimisation, data science and quantitative coursework.
First technical bridge into programming, data structures and machine-learning workflows.
Introduction to how data is organised, stored, queried and prepared for analysis.
Initial orientation on artificial intelligence concepts, limits and responsible use.
A first look at how computers execute, store and process information.
Essential networking concepts for students who will work with connected systems and platforms.
Hands-on system practice to consolidate the IT foundation of the programme.
Sustainable IT awareness and responsible digital practice.
Baseline spreadsheet skills before advanced analytics and visualisation coursework.
This is the analytical heart of the programme: mathematics, statistics, machine learning, modelling, interpretation and domain applications.
Calculus, linear algebra and complex numbers required for optimisation and data science.
Descriptive statistics, probability theory and applications using R.
Importing, manipulating, transforming, visualising, exploring and modelling large datasets in R.
Data preparation, feature engineering and machine-learning modelling with Python libraries.
RDF, SPARQL and web standards for representing, querying and using knowledge on the web.
Temporal data analysis with mathematical foundations and applications in R for forecasting.
Complex problem modelling using ABM, with comparison to statistical, Markov and system dynamics approaches.
Tests, confidence intervals, models, assumptions, linear models and regression.
Practical analytics applications across sectors such as marketing, finance, industry and risk management.
A data analyst with IT independence must understand databases, data warehouses, pipelines, cloud and software principles.
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.
XML data flow, DTD and schemas, XSL transformations and JSON data formats.
AWS cloud services and preparation for AWS Certified Solutions Architect – Associate.
Procedural programming in Rust, memory representation, ownership, borrowing and reliable code.
Object-oriented programming in C#, SOLID principles, UML, MVC, design patterns and Entity Framework.
This block connects analytics to the tools and systems that organisations actually use for reporting, CRM and decision support.
Formulas, data visualisation, PowerPivot, Solver and Visual Basic for Applications.
Data visualisation and machine-learning ecosystem, with a focus on SAS Viya.
Principles and methodologies for designing and analysing information systems.
Preparation for Microsoft Power BI data analysis certification and reporting workflows.
Microsoft Power Platform Functional Consultant preparation and CRM data management software.
Analytics professionals also need to understand the organisational, legal and ethical environment in which data is used.
Data privacy and security principles, EU and US regulation, and differences between common law and code law.
Project management lifecycle and implementation using traditional and agile approaches.
The programme includes structured support and professional experience so that knowledge is consolidated and applied.
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 Analytics, DSTI foregrounds certifications prepared through CRM data management, Power BI reporting, SAS visual statistics and AWS cloud computing. Other recognised certifications may be accepted only when Faculty and Direction of Studies validate their level and relevance.
Course context: CRM Data Management
Certification websiteCourse context: Reporting and Visualisation
Certification websiteCourse context: Data & Machine Learning Visualisation
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++ InstituteMicrosoft
GoogleGoogle
Tableau
dbtMicrosoftMicrosoftMicrosoft
Elastic
OracleOracle
QlikMicrosoftMicrosoft
scikit-learnSASSASDSTI was built around connected teaching. Students can study on campus, through Live Streamed access, or through the online asynchronous route when this mode fits their situation and eligibility.
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 Data Analyst, Business Intelligence Analyst, Reporting Analyst or CRM / Data Management Analyst, depending on the student’s background and internship project.
With experience, graduates can move towards Analytics Consultant, Analytics Engineer, Data Product Analyst or team-level analytics responsibilities. The programme does not shortcut the need for proven delivery.
The MSc in Data Analytics with AI is open to a wider range of Bachelor backgrounds than more specialised engineering routes, but mathematics, English and technical readiness still matter.
Applicants should hold a recognised 3 or 4-year Bachelor degree or equivalent. Mathematics at high-school level or equivalent is expected.
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.
If you are hesitating between Data Analytics, 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.
