# Executive MSc in Artificial Intelligence for Digital Transformation. Canonical HTML: https://dsti.school/executive-msc-artificial-intelligence-digital-transformation 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. Executive MSc • Artificial Intelligence • Digital Transformation A master’s level route for experienced professionals who need to understand, evaluate and lead AI-enabled transformation without pausing their career. DSTI brings its engineering-school strength to executive education: AI, data, cloud, analytics and information systems are taught as connected capabilities for professionals who must make informed organisational decisions. Apply now Discuss your project Study while working Download brochure Designed for professionals Typically 30s, 40s and beyond, already working and often already managing projects, teams or clients. Online asynchronous first Study at your pace with recorded resources, digital learning materials and structured academic support. DSTI credentials Hundreds of experienced professionals have already graduated through DSTI’s flexible model. Programme centre of gravity AI competence for digital transformation, grounded in data and systems. For professionals who need to lead informed AI choices: what to build, what to buy, what to automate, what to govern and what to question. 120 ECTS Master’s level programme • RNCP level 7 500h Classes and practical work Flexible Online asynchronous route central 01 — Executive positioning ## For professionals ready to turn AI into organisational capability. The Executive MSc is built for people who have already accumulated professional judgement. The objective is to add serious AI, data and digital systems competence to an existing career, so strategic decisions can be grounded in technical reality. Why this route exists ## Digital transformation now requires technical literacy at leadership level. Successful AI transformation depends on professionals who can discuss data quality, architecture, automation, modelling limits, governance, security and implementation with technical teams and business stakeholders. Engineering-school depth ### Technical substance for executive decisions The programme speaks to professionals who need to understand how AI systems are built, integrated, evaluated and governed in real organisations. Master’s level structure ### Broad, serious and connected The curriculum connects AI, data science, data engineering, cloud, analytics, information systems and cyber security at executive pace. Hybrid professional profiles ### For leaders between business and technology Managers, consultants, engineers, analysts, entrepreneurs and transformation leads can all benefit when their project requires durable AI competence. 02 — Who it is for ## A programme for people with professional gravity. The tone, rhythm and learning experience should match experienced adults: clear, demanding, flexible, respectful of time and directly connected to real organisational problems. 01 ### Managers and transformation leads Professionals who need to lead AI or digital transformation projects without depending entirely on vendors or specialist teams. 02 ### Consultants and project directors People advising organisations on digital, data or automation choices who need stronger technical credibility. 03 ### Engineers and IT professionals Technical profiles moving towards architecture, leadership, governance, product ownership or AI-enabled operations. 04 ### Entrepreneurs and senior specialists Professionals building products, services or internal capabilities where AI, data and information systems must be understood together. 03 — Flexibility ## Online asynchronous is not secondary here. It is central. For this audience, flexibility is not a convenience; it is the condition that makes serious study possible while maintaining professional and personal responsibilities. Primary experience for many executive learners ## Study online, asynchronously, at a professional rhythm. DSTI’s model gives experienced learners access to structured academic content, recordings, digital resources and assessment routes without forcing a full career interruption. The programme is designed so that learning can be organised around work, travel, family and project responsibilities. Recorded learning ### Revisit complex material AI, statistics, cloud and data systems often require repeated viewing and practice. Asynchronous learning supports that reality. Live connection where useful ### Support without rigid attendance logic Live streamed access and support sessions can complement the asynchronous route when available and relevant. Same academic seriousness ### Flexibility does not dilute expectations The objective remains master’s level competence, assessed work and professional application, not passive content consumption. 04 — Programme structure ## Four technical pillars, one transformation objective. The programme combines 500 hours of classes and practical work with integrated professional experience. The structure gives professionals a broad, connected understanding of AI-enabled digital systems. 500h Classes and practical work 120 ECTS master’s level programme 4 Core teaching units 4–6 Months professional experience 05 — Curriculum ## Transparent programme content, course by course. The Executive MSc keeps a broad, connected curriculum: data science, data engineering, cloud and cyber security, analytics and professional application. Course cards below show the latest programme content with hours, support hours where applicable and ECTS. Four teaching units ### Data, AI and digital systems The programme is organised around the technical foundations that executives need to evaluate and lead AI-enabled transformation. Course-level transparency ### Not just broad labels Each teaching unit opens into course cards showing the academic content, hours, support hours and ECTS. Executive relevance ### Technical depth for decision-makers The curriculum is designed for experienced professionals who need enough technical understanding to lead, question and apply responsibly. Data Science 5 courses ### Mathematics, statistics, machine learning and knowledge representation This unit builds the quantitative and modelling foundation needed to understand modern AI systems rather than only use them at surface level. 25h 5h support 4 ECTS #### Applied Mathematics for Data Science This course covers the basic notions of applied mathematics required to study optimisation for data science: calculus, linear algebra and complex numbers. 25h 5h support 4 ECTS #### Foundations of Statistical Analysis and Machine Learning - Part 1 A course that introduces the fundamentals of descriptive statistics, probability theory, and their applications using the R programming language for data analysis. 25h 6 ECTS #### Artificial Neural Networks Neural network layers, weights, biases, hyperparameters and optimisation algorithms, with classification and regression applications implemented in Python using TensorFlow. 25h 5h support 6 ECTS #### Python Machine Learning Labs Data structures, data cleaning and preparation techniques, feature engineering and machine-learning modelling with Python libraries. 25h 5h support 6 ECTS #### Semantic Web Technologies RDF and SPARQL for representing and querying web-rich data, as well as using knowledge on the web with standardised frameworks. Data Engineering 5 courses ### Information systems, SQL, graph data and pipelines This unit connects AI and analytics to the data systems, information-system design and project methods needed in real organisations. 25h 4 ECTS #### Analysis & Design of Information Systems Principles and methodologies behind designing and analysing information systems. 25h 3 ECTS #### IT Project Management: Traditional and Agile Approaches Project management lifecycle and best practices for working with traditional and Agile approaches. 25h 5h support 6 ECTS #### Data Wrangling with SQL Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and applications with Microsoft SQL Server. 25h 4 ECTS #### Graph Databases NoSQL Part 1 Preparation for the Neo4j Certified Professional certification and graph-based problem modelling with practical implementation in Neo4j. 25h 5h support 4 ECTS #### Data Pipeline Part 1 XML data flow, DTD and schemas, XSL transformations and JSON data formats for building efficient and scalable data pipelines. Cyber Security & Cloud Engineering 4 courses ### Cloud platforms, cyber security and regulated digital systems This unit gives executive learners the cloud, security, data-platform and regulatory awareness required to evaluate AI-enabled transformation responsibly. 50h 9 ECTS #### Cloud Computing - Amazon AWS Use different cloud services on the AWS platform and prepare for the AWS Certified Solutions Architect – Associate certification. 25h 4 ECTS #### Fundamentals of Cyber Security Practices System security design patterns, infrastructure security, encryption for data at rest and in transit, and code safety. 25h 5h support 4 ECTS #### Data Pipeline Part 2 Apache Spark, Kafka, modern data-platform components, Lambda and Kappa architectures, “anything as code” and CI/CD practices. 25h 3 ECTS #### Data Laws & Regulations - Philosophies, Geopolitics & Ethics Principles and frameworks of data privacy and security, including EU and US regulations and common-law/code-law differences. Data Analytics 5 courses ### Business analytics, reporting, CRM and domain applications This unit connects the technical curriculum to reporting, dashboards, customer data, domain problems and management-oriented analytical work. 5h 2 ECTS #### Warm Up Refreshers on AI Awareness. 25h 5h support 6 ECTS #### Advanced Excel for Data Analytics Formulas, data visualisation, PowerPivot, Solver and Visual Basic for Application. 25h 6 ECTS #### Reporting & Visualisation Introduction to the modules used in preparation for the Analysing Data with Microsoft Power BI certification. 25h 5 ECTS #### Data Analytics Domain Applications Practical domain applications for data analytics in marketing, finance, industry and risk management. 25h 4 ECTS #### CRM Data Management Preparation for Microsoft Power Platform Functional Consultant (PL-200) and introduction to CRM data-management software. 06 — Professional certification ## A focused certification path, aligned with the programme. Executive MSc students must validate Neo4j. A second approved certification is optional but recommended; it grants the “with Honours” distinction to graduates. Executive MSc rule ### Neo4j is mandatory. A second targeted certification can strengthen the graduate profile. The programme is short and focused, so the certification pathway remains clear: Neo4j validates the graph-database component, while AWS or Microsoft PL-200 may be chosen as an optional second certification when relevant to the learner’s role and objectives. Mandatory Neo4j certification for every Executive MSc student. With Honours A second approved certification grants the “with Honours” graduate distinction. Optional focus AWS Certified Solutions Architect – Associate or PL-200: Microsoft Power Platform Functional Consultant. Neo4j ### Neo4j certification Course context: Graph Databases NoSQL Part 1 Certification website AWS ### AWS Certified Solutions Architect – Associate Course context: Cloud Computing - Amazon AWS Certification website Microsoft ### PL-200: Microsoft Power Platform Functional Consultant Course context: CRM Data Management Certification website 07 — DSTI credentials ## DSTI was built for this kind of learner. Before hybrid education became fashionable, DSTI was already designed as one connected engineering school for working adults, international learners and professionals who could not always relocate or stop working. Hundreds of graduates ### Experienced professionals already went through DSTI DSTI has graduated many learners in their 30s, 40s and beyond, including professionals balancing study with work and family commitments. Engineering school culture ### AI connected to systems The programme is grounded in data, software, cloud, analytics and cyber security rather than isolated management narratives about innovation. Professional respect ### Adult learning without infantilising the audience The page, tone and structure should speak to people who already have careers, constraints and decision-making responsibility. 08 — Graduate gallery ## Graduates who studied with work, responsibility and ambition. DSTI’s executive and professional learners often study while managing teams, clients, organisations and families. This gallery is designed to highlight real graduate trajectories: experienced adults who used rigorous, flexible study to strengthen their role in AI-enabled transformation. Spring 2019 ### Transformation leader Olivier Gschwind LinkedIn profile Autumn 2019 ### Consulting or project direction Razan Nofal LinkedIn profile Autumn 2025 ### Engineering or IT leadership Arun Chinega LinkedIn profile Autumn 2022 ### Data and analytics responsibility Diego Carriel Lopez LinkedIn profile Spring 2024 ### Entrepreneurial pathway Guillaume Pealat LinkedIn profile Autumn 2019 ### Career repositioning Lorena Romeo LinkedIn profile 09 — Careers ## Use the programme to structure a real professional move. The integrated professional experience is designed to connect learning with a concrete AI, data or digital transformation project, whether through a role, mission, internship, consultancy project or validated professional setting. 30 ECTS ## Professional application matters. The final professional experience validates the ability to use AI and digital systems knowledge in an organisational context. For executive learners, this often means connecting the programme to an existing career direction or transformation project. Career transition ### Move towards AI-enabled leadership Use the programme to reposition from general management, consulting, engineering, operations or IT towards AI and digital transformation. Current employer ### Build relevance where you already work For some professionals, the strongest project may be linked to their current organisation and validated academically. Near-term application ### Strengthen an existing role or project Common outcomes include AI Project Owner, Data / AI Product Owner, Digital Transformation Project Lead or analytics manager responsibilities within an existing organisation. Progression path ### Move towards transformation leadership With seniority and proven delivery, the programme can support AI Transformation Lead, Digital Transformation Manager or Head of Data / AI initiatives. It is not positioned as an instant executive-title shortcut. 10 — Admissions ## Selective admission for experienced professionals. Admissions should assess both academic readiness and professional coherence. This programme is intended for mature learners with a clear reason to develop AI and digital transformation competence. Academic level ### Master’s level background expected Applicants are typically already master’s-level graduates or able to demonstrate equivalent academic readiness. Professional experience ### Normally 4+ years Professional experience should show maturity, responsibility and a plausible connection to AI, data, digital systems or transformation work. Bridging where needed ### Online entry exam if appropriate Applicants whose prior studies did not include sufficient mathematics, statistics or IT may be asked to take DSTI’s online entry examination. English ### Professional working level The programme is taught in English. Applicants should be able to study technical material and communicate professionally in English. ## Considering AI for your next professional chapter? Bring your professional context to the discussion. The right question is not only whether you are interested in AI, but what kind of AI-enabled transformation you need to understand, lead or evaluate. Book a meeting Apply now Review flexibility Download brochure On this page