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.
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.
The programme is organised around the technical foundations that executives need to evaluate and lead AI-enabled transformation.
Each teaching unit opens into course cards showing the academic content, hours, support hours and ECTS.
The curriculum is designed for experienced professionals who need enough technical understanding to lead, question and apply responsibly.
This unit builds the quantitative and modelling foundation needed to understand modern AI systems rather than only use them at surface level.
This course covers the basic notions of applied mathematics required to study optimisation for data science: calculus, linear algebra and complex numbers.
A course that introduces the fundamentals of descriptive statistics, probability theory, and their applications using the R programming language for data analysis.
Neural network layers, weights, biases, hyperparameters and optimisation algorithms, with classification and regression applications implemented in Python using TensorFlow.
Data structures, data cleaning and preparation techniques, feature engineering and machine-learning modelling with Python libraries.
RDF and SPARQL for representing and querying web-rich data, as well as using knowledge on the web with standardised frameworks.
This unit connects AI and analytics to the data systems, information-system design and project methods needed in real organisations.
Principles and methodologies behind designing and analysing information systems.
Project management lifecycle and best practices for working with traditional and Agile approaches.
Relational databases, advanced SQL queries, stored procedures, triggers, dynamic SQL and applications with Microsoft SQL Server.
Preparation for the Neo4j Certified Professional certification and graph-based problem modelling with practical implementation in Neo4j.
XML data flow, DTD and schemas, XSL transformations and JSON data formats for building efficient and scalable data pipelines.
This unit gives executive learners the cloud, security, data-platform and regulatory awareness required to evaluate AI-enabled transformation responsibly.
Use different cloud services on the AWS platform and prepare for the AWS Certified Solutions Architect – Associate certification.
System security design patterns, infrastructure security, encryption for data at rest and in transit, and code safety.
Apache Spark, Kafka, modern data-platform components, Lambda and Kappa architectures, “anything as code” and CI/CD practices.
Principles and frameworks of data privacy and security, including EU and US regulations and common-law/code-law differences.
This unit connects the technical curriculum to reporting, dashboards, customer data, domain problems and management-oriented analytical work.
Refreshers on AI Awareness.
Formulas, data visualisation, PowerPivot, Solver and Visual Basic for Application.
Introduction to the modules used in preparation for the Analysing Data with Microsoft Power BI certification.
Practical domain applications for data analytics in marketing, finance, industry and risk management.
Preparation for Microsoft Power Platform Functional Consultant (PL-200) and introduction to CRM data-management software.
Executive MSc students must validate Neo4j. A second approved certification is optional but recommended; it grants the “with Honours” distinction to graduates.
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.
Course context: Cloud Computing - Amazon AWS
Certification websiteCourse context: CRM Data Management
Certification websiteDSTI’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.
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.
Applicants are typically already master’s-level graduates or able to demonstrate equivalent academic readiness.
Professional experience should show maturity, responsibility and a plausible connection to AI, data, digital systems or transformation work.
Applicants whose prior studies did not include sufficient mathematics, statistics or IT may be asked to take DSTI’s online entry examination.
The programme is taught in English. Applicants should be able to study technical material and communicate professionally in English.
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.
