# MSc in Data Analytics with Artificial Intelligence. Canonical HTML: https://dsti.school/msc-in-data-analytics-with-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 Analytics • Artificial Intelligence • IT automation A postgraduate route for students who want to turn data into decisions, automation and operational impact — with enough AI, IT and engineering culture to work inside real digital systems, not only produce reports on the side. This is the analytics route for students who want independence: understanding the data, the tools, the systems and the organisational context well enough to make analytics useful in production. Apply now Book a meeting Compare MSc routes Download brochure Beyond dashboards Analytics that can be implemented, automated and maintained. AI-aware analytics Machine learning and modelling without losing decision context. IT independence SQL, cloud, data pipelines, CRM, reporting and software principles. Programme centre of gravity Data-driven decisions, implemented in real information systems. For students who want to understand the analytical method and the technical environment behind it. 120 ECTS Master’s level programme • RNCP level 7 835h Total programme volume 3IA-labelled programme Labelled by the 3IA programme of Université Côte d’Azur. 01 — Programme identity ## Analytics is not only dashboards. It is implementation. This programme is designed for students who want a data analytics profile that is technically credible, practical and connected to the IT and automation chain. DSTI positioning ## From insight to action. Many analytics programmes stop at reporting and business interpretation. DSTI’s approach goes further: statistical thinking, AI awareness, database skills, data engineering, software principles, cloud culture and information-system understanding. Analytics ### Make data usable for decisions Learn to structure data, analyse it, visualise it and communicate evidence clearly. AI ### Use modelling responsibly Machine learning and statistical foundations help you understand what models can and cannot do. IT ### Integrate into digital systems SQL, data pipelines, cloud, CRM, reporting tools and software principles make graduates more independent. Automation ### Move beyond manual analysis The goal is not only to produce insight, but to help operationalise analytics in real workflows. 02 — Who this programme is for ## For students who want analytical independence, not a narrow tool course. The MSc in Data Analytics with AI is DSTI’s most open MSc route, while remaining serious about mathematics, IT and technical progression. Good fit ### You want a credible data role connected to business, IT and automation. - You like data-driven decision-making and practical implementation. - You want to understand databases, reporting tools, Python, R, SQL and cloud environments. - You want enough machine learning and AI to use models intelligently. - You may come from business, management, economics, engineering, science or another quantitative background. Less ideal if ### Your main goal is deep model research or infrastructure engineering. - If your main interest is advanced modelling and AI systems, compare with MSc in Data Science & AI. - If your main interest is data platforms, cloud architecture and pipelines, 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. 720h Core taught coursework across analytics, data engineering, visualisation, information systems, management, law and ethics. 75h DSTI Warm Up to reinforce mathematics, programming, AI awareness, IT foundations and systems practice. 40h 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 Analytics with AI 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. Core coursework • 720h ### Analytics, AI and IT depth A transparent course-by-course structure covering analysis, modelling, data systems, visualisation 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 ### Foundations before the MSc core The Warm Up helps students from varied backgrounds reinforce the technical foundations needed to start the programme seriously. 10h #### Fundamental Mathematics Core mathematics needed to approach optimisation, data science and quantitative coursework. 20h #### Data Structure and Machine Learning using Python & R First technical bridge into programming, data structures and machine-learning workflows. 5h #### Data Management Introduction to how data is organised, stored, queried and prepared for analysis. 5h #### AI Awareness Initial orientation on artificial intelligence concepts, limits and responsible use. 5h #### Computer Architecture A first look at how computers execute, store and process information. 5h #### Networking Essential networking concepts for students who will work with connected systems and platforms. 10h #### Computer Systems Labs Hands-on system practice to consolidate the IT foundation of the programme. 10h #### Clean IT Sustainable IT awareness and responsible digital practice. 5h #### Excel Basics Baseline spreadsheet skills before advanced analytics and visualisation coursework. Data Analytics 225h • 30 ECTS ### Statistics, machine learning, modelling and domain use This is the analytical heart of the programme: mathematics, statistics, machine learning, modelling, interpretation and domain applications. 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 & Machine Learning — Part 1 Descriptive statistics, probability theory and applications using R. 25h 3 ECTS #### Big Data Processing with R Importing, manipulating, transforming, visualising, exploring and modelling large datasets in R. 25h 4 ECTS #### Python Machine Learning Labs Data preparation, feature engineering and machine-learning modelling with Python libraries. 25h 4 ECTS #### Semantic Web Technologies RDF, SPARQL and web standards for representing, querying and using knowledge on the web. 25h 3 ECTS #### Time-Series Analysis Temporal data analysis with mathematical foundations and applications in R for forecasting. 25h 4 ECTS #### Agent-Based Modelling Complex problem modelling using ABM, with comparison to statistical, Markov and system dynamics approaches. 25h 3 ECTS #### Inferential Modelling Tests, confidence intervals, models, assumptions, linear models and regression. 25h 3 ECTS #### Data Analytics Domain Application Practical analytics applications across sectors such as marketing, finance, industry and risk management. Data Engineering 205h • 33 ECTS ### The systems and pipelines behind analytics A data analyst with IT independence must understand databases, data warehouses, pipelines, cloud and software principles. 25h 3 ECTS #### Data Wrangling with SQL Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and Microsoft SQL Server. 25h 3 ECTS #### Data Warehousing & ETL Design and implementation of data warehouses and ETL processes in stand-alone and cluster deployments. 25h 4 ECTS #### Graph Databases — NoSQL Part 1 Graph-based problem modelling and practical implementation in Neo4j graph databases. 10h 4 ECTS #### Document Databases — NoSQL Part 2 MongoDB collections, documents, advanced queries, aggregations and data architecture. 25h 4 ECTS #### Data Pipeline — Part 1 XML data flow, DTD and schemas, XSL transformations and JSON data formats. 50h 4 ECTS #### Cloud Computing — Amazon AWS AWS cloud services and preparation for AWS Certified Solutions Architect – Associate. 25h 3 ECTS #### Software Engineering — Part 1 Procedural programming in Rust, memory representation, ownership, borrowing and reliable code. 25h 3 ECTS #### Software Engineering — Part 2 Object-oriented programming in C#, SOLID principles, UML, MVC, design patterns and Entity Framework. Data Management & Visualisation 125h • 22 ECTS ### Decision support, reporting and information systems This block connects analytics to the tools and systems that organisations actually use for reporting, CRM and decision support. 25h 4 ECTS #### Advanced Excel for Data Analytics Formulas, data visualisation, PowerPivot, Solver and Visual Basic for Applications. 25h 4 ECTS #### Data & Machine Learning Visualisation Ecosystem Data visualisation and machine-learning ecosystem, with a focus on SAS Viya. 25h 2 ECTS #### Analysis & Design of Information Systems Principles and methodologies for designing and analysing information systems. 25h 6 ECTS #### Reporting & Visualisation Preparation for Microsoft Power BI data analysis certification and reporting workflows. 25h 6 ECTS #### CRM Data Management Microsoft Power Platform Functional Consultant preparation and CRM data management software. Management, Ethics & Law 50h • 4 ECTS ### Governance, law and project context Analytics professionals also need to understand the organisational, legal and ethical environment in which data is used. 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 implementation using traditional and agile approaches. Support & Professional Experience 40h support • 30 ECTS experience ### Consolidation and workplace application The programme includes structured support and professional experience so that knowledge is consolidated and applied. 40h #### Support Sessions Reviewing course topics, answering student questions, re-explaining harder notions 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 ## Professional certification with an employability signal. 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. Programme certifications are prepared inside the courses. 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. 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 Microsoft ### Exam PL-200: Microsoft Power Platform Functional Consultant Course context: CRM Data Management Certification website Microsoft ### Exam PL-300: Microsoft Certified: Power BI Data Analyst Associate Course context: Reporting and Visualisation Certification website SAS ### Modeling Using SAS Visual Statistics Course context: Data & Machine Learning Visualisation Certification website AWS ### AWS Certified Solutions Architect – Associate Course context: Cloud Computing - Amazon AWS 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 Microsoft #### Microsoft Office Specialist: Excel Associate Advanced Excel for Data Analytics Official page Google #### Google Data Analytics Professional Certificate Big Data Processing with R Official page Google #### Google Cloud Data Analytics professional Certificate Cloud data analytics Official page Tableau #### All Tableau certifications Data visualisation and analytics Official page dbt #### dbt Analytics Engineering Certification Exam Analytics engineering Official page Microsoft #### Microsoft Certified: Power Platform Developer Associate CRM Data Management Official page Microsoft #### Exam MO-211: Microsoft Excel Expert Advanced Excel for Data Analytics Official page Microsoft #### Microsoft Office Specialist: Excel Expert Advanced Excel for Data Analytics Official page Elastic #### Elastic Certified Analyst Analytics search and observability Official page Oracle #### Oracle Database SQL Certified Associate Datawarehousing & ETL Official page Oracle #### MySQL 8.0 Database Developer Oracle Certified Professional Datawarehousing & ETL Official page Qlik #### Qlik Sense Business Analyst Certification Datawarehousing & ETL Official page Microsoft #### Microsoft Certified: Fabric Analytics Engineer Associate Microsoft Fabric analytics Official page Microsoft #### Microsoft Excel Certification Advanced Excel for Data Analytics Official page scikit-learn #### Scikit-learn Certification (all levels accepted) Machine learning with Python Official page SAS #### SAS Certified Specialist: Machine Learning Using SAS Viya Data & Machine Learning Visualisation Official page SAS #### SAS Viya Supervised Machine Learning Pipelines Data & Machine Learning Visualisation 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 asynchronous route when this mode fits their situation and eligibility. 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 ## Analytics becomes real when it is used in organisations. 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 professional 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 professional steps ### Analyst and BI roles 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. Progression path ### Grow through delivered analytics work 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. 08 — Admissions ## Selective admission, with a route for varied backgrounds. 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. Eligibility ### Bachelor degree or equivalent Applicants should hold a recognised 3 or 4-year Bachelor degree or equivalent. Mathematics at high-school level or equivalent is expected. Entry exam ### Used when helpful Applicants from less technical backgrounds may be asked to take DSTI’s online entry exam in mathematics and IT. 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): Analysis & Design of Information Systems. - Analysis & Design of Information Systems - 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 Analytics with AI the right route for you? 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. Book a meeting Apply now Compare MSc routes On this page