Data Science and Management
- Degree
- Graduate school
- Credits
- 120 ECTS
- Duration
- 2 Years
- Language
- English
- Year
- 2027/2028
The Luiss Master’s Degree in Data Science and Management, part of the LM-Data class, trains professionals who can integrate data science, artificial intelligence, and management to drive innovation and support strategic decisions within organisations.
The course, delivered entirely in English, provides comprehensive and well-balanced training that combines quantitative, IT, and managerial skills. In a context marked by digitalisation and the increasing use of data, the Programme meets the demand for professionals who can analyse complex phenomena, transform vast amounts of information into knowledge, and use data to improve business processes, products, and strategies.
The teaching adopts a practical and applied approach, incorporating workshops, case analyses, projects, and the use of advanced technological tools. Students learn to develop and apply data-driven methodologies across various business areas, from logistics to production, marketing to finance, and even human resource management and research and development.
In line with the educational objectives of the LM-Data master's degree programme, the course prepares professionals who are capable of:
- Integrate digital technologies and mathematical-statistical methodologies with management tools to support decision-making processes and strategic innovation.
- Interpreting and guiding digital and organisational innovation processes, promoting the responsible adoption of data-driven models in businesses and public administrations.
- Design and evaluate innovative solutions based on data analysis and artificial intelligence to tackle complex, interdisciplinary problems.
- Operate with independent judgement, ethical awareness, and a strategic vision, considering the regulatory and social implications of data use.
- Work with and lead interdisciplinary teams, acting as a bridge between technical and decision-making expertise.
The Director
Blerina Sinaimeri
She is currently Associate Professor of Computer Science at LUISS Guido Carli University. Before joining LUISS, she was a tenured researcher (Chargée de recherche) at INRIA, the French National Institute for Research in Digital Science and Technology. She earned her PhD in Computer Science from Sapienza University of Rome in 2010.
Her research focuses on the algorithmic and computational aspects of graph theory, with applications to computational biology and the social sciences, particularly the study of the evolution of interacting systems. She has published in leading international journals, including Algorithmica, Theoretical Computer Science, Bioinformatics, and IEEE/ACM Transactions on Computational Biology and Bioinformatics.
She has taught at both undergraduate and graduate levels in Italy and France, including at LUISS, Université Lyon 1, the École Normale Supérieure de Lyon (ENS), and the Institut National des Sciences Appliquées de Lyon (INSA). She is also actively engaged in science outreach and public engagement initiatives.
The curriculum allows you to build an academic and professional profile that aligns with your interests and career goals, combining specialist training, a personalised pathway, and learning through real-world experiences.
The educational offering is structured as follows:
- Essentials: courses that provide the theoretical, methodological, and disciplinary foundations of the degree programme, developing the core knowledge upon which to build the subsequent specialisation pathway.
- Majors (where included in the study plan): in-depth courses dedicated to specific disciplinary or professional areas, through which students can develop advanced and distinctive skills in their field of interest.
- Profile: elective courses that allow you to further personalise your learning path by selecting content that aligns with your academic and professional aspirations.
- Certificates: These are distinctive features of the Graduate School’s educational model, integrated into the curriculum to give students the opportunity to choose learning activities developed in collaboration with Corporate and Institutional Partners. With a strong focus on practical application, the Certificates enable students to gain specialised skills, engage with industry professionals, and delve deeper into methodologies and experiences gained across various professional settings. Upon completion of each Certificate, a notarised digital micro-credential is issued on the blockchain. This securely and verifiably attests to the skills acquired and can be shared on major professional profiles, enhancing one's educational background.
- Field Experience: real-world professional experiences that allow you to put the skills you've developed during your studies into practice and foster the connection between your academic training and the world of work.
- Final Thesis: the culmination of the educational journey, dedicated to research, critical analysis, and the application of acquired knowledge.
🇬🇧 Data Science and Management
Semester I - Essentials
Data-Driven Management
The course analyses how artificial intelligence and big data support managerial decisions in the main business areas - from logistics to production, from marketing to research and development, to finance and human resources management. The factors favouring their adoption and impact on decision-making processes are explored.
Data Science in Action
The course is designed to act as a missing link between model-based analysis and data-centric techniques. It uses numerous examples of real-life event logs to illustrate the concepts and algorithms presented in the other courses.
Data Visualization
The course provides an overview of the principles and tools of data visualisation. Accordingly, students learn how data analysis and visualisation work together to effectively communicate data-based results, motivate analysis and detect errors.
Statistical Foundations of Data Science
The course provides an overview of advanced statistical methods for data science, with emphasis on understanding the advantages and limitations of each approach, their interpretation, and their main applications in economics, business and management.
Computing Infrastructures for Data Science
The course introduces the fundamental concepts of computational infrastructures for data science. Students will become familiar with hardware and software architectures, distributed systems, cloud computing, and tools for the efficient management of large amounts of data, with a focus on scalability, reliability and performance aspects.
Semester II - Essentials & Major
Ethics for AI
The course focuses on the ethical issues of the most recent developments in artificial intelligence, with particular emphasis on algorithmic judgment.
Deep Learning
The course offers a comprehensive introduction to neural networks and deep learning. The main architectures (e.g. feedforward, convolutional and recurrent networks) and fundamental training techniques will be covered. Practical applications and experiential labs will complement the theoretical concepts.
Regulation for AI
The course builds a solid foundation on privacy and data protection law from a European and comparative perspective. It provides the tools to fairly and responsibly manage the processing of personal data in organisations.
Data Privacy and Security
The course provides essential knowledge on data privacy and cybersecurity, combining theoretical concepts with practical skills. Students will explore the fundamentals of modern cryptography, secure network communication, blockchain technologies and the main vulnerabilities of modern software.
Digital Ecosystems
The course reviews and analyses current theories of ecosystems in the fields of information systems, organisational studies, business strategy and innovation. Specific attention is dedicated to ecosystems that develop around the production, sharing, analysis and exchange of data.
Optimization Methods in Management Science
The course introduces students to the theory, algorithms and applications of optimisation. Methodologies covered include linear programming, optimisation over networks, integer programming and decision trees. Applications to various areas of business and management are covered.
Semester I - Essentials
Statistical Learning
The course covers advanced regression and classification methods for flexible modelling of complex data. The main topics covered include non-parametric techniques, Gaussian processes, kernel-based approaches and Reproducing Kernel Hilbert Spaces (RKHS), with a focus on possible applications to different areas of business and management.
Foundation Models
The course introduces students to the foundation models that power the most recent applications of artificial intelligence, in particular large language models (LLM). Architectures, pretraining techniques, scalability and adaptation to specific application contexts are analysed.
Profile
2 elective courses
Certificate
Certificate 1
Certificate 2
Field Experience
Internship
Create
Final Thesis
Total ECTS
Luiss offers various opportunities for international experience and the development of skills that are valuable in global academic and professional settings.
Students can take part in Double and Triple Degree Programmes, QTEM Masters Network programmes, Erasmus and International Exchanges, Free Mover semesters abroad, international Summer Schools, the Joint Programme in Digital Transformation, and ENGAGE.EU international mobility programmes.
Admission depends on the course of study and the requirements set out in the individual calls for applications. Explore all the international experiences offered by Luiss and choose the one that best aligns with your educational and professional goals.
Admission procedures and requirements for 2027/2028
To enrol in the Master’s Degree in Data Science and Management at Luiss, you must follow the admission procedures for the 2027/2028 academic year.
Initial information on admission procedures, selection sessions, and key deadlines is available in the dedicated section. The notices, which will include all requirements, how to participate, and the full calendar, will be published from November 2026.
Visit the page dedicated to admission to Luiss Master’s Degree Courses and find the procedure that applies to your profile.
Tuition fees
The single annual fee for enrolment in the first year of the Master’s Degree in Data Science and Management for the 2027/2028 academic year is €16,000, payable in three instalments, plus the regional tax.
Scholarships and financial support
Luiss offers scholarships and partial or full exemptions from tuition fees, thanks in part to support from public and private partners. This enables students to access tangible support throughout their academic journey.
Explore all the opportunities for Luiss scholarships and financial aid, and check the requirements, deadlines, and how to apply.
The Master's Degree in Data Science and Management trains new business leaders who can effectively apply data-driven methodologies across multiple organisational areas.
Graduates acquire quantitative, IT, and managerial skills that enable them to analyse complex phenomena, manage large data flows, and integrate data science methodologies into companies' strategic and operational processes.
Key career opportunities include:
- AI & Data Scientist: professionals who are experts in solving complex problems using advanced quantitative methodologies and IT techniques, with the aim of extracting knowledge and strategic value from data;
- Data Intelligence Analyst: professionals capable of integrating data science methodologies into a company’s strategic and operational processes;
- Data Managers: responsible for managing, collecting, and processing large data flows, with expertise in assessing their reliability, privacy, and security.
- AI Product Manager: a role that leads the development and adoption of products based on artificial intelligence, ensuring that technological solutions are effective and sustainable.
Graduates and employability
The employment rate for Luiss graduates one year after graduation is 95%. The average waiting time between graduation and the first job is one month, and 70% of graduates receive a job offer before they graduate. 12% work abroad.
Luiss also has an active network of over 500 employers, including companies, multinationals, and public and private institutions, which offer tangible professional opportunities to students and recent graduates.
Luiss Alumni Network
With over 70,000 alumni worldwide, the Luiss Alumni Network is a dynamic and ever-growing community, created to keep the connection with the university alive after graduation. With 19 active international chapters, alumni and students take part in local initiatives that strengthen their sense of belonging and foster intergenerational dialogue.
The activities on offer include professional discussions, networking events, opportunities for training and in-depth study on current issues, class reunions, cultural visits, and initiatives aimed at enhancing the Alumni community.
Discover the Luiss Graduate School’s Employability Journey and the initiatives designed to support students as they develop their skills, build their professional networks, and enter the world of work.
