Bring your data challenge to our MSc Capstone

The MSc in Computing for Data Science at the Free University of Bozen-Bolzano invites companies to propose real-world, data-driven projects for our students. A Capstone gives your organization the opportunity to explore a concrete challenge while helping students apply their scientific and technical skills to data that matter.

Interested in Capstone Days 2026? Please contact us as soon as possible. Company presentations take place in early October 2026, in English, either in person or remotely via Microsoft Teams. Allow approximately 15 minutes for each project; companies are welcome to present more than one proposal.

6 creditsPart of the second year of the MSc.

About 150 hoursStudent work for each project.

1 student or pairIndependent work with two supervisors.

How the collaboration works

Each project is supported by two complementary figures:

A company domain expert introduces the data and application context, defines requirements, answers questions, and provides ongoing feedback.
A faculty tutor supervises and guides the work and carries out the final evaluation under a pass/fail scheme.

Propose a use-case

Share a focused challenge, its context, the available data, and a feasible scope.

Present it

Introduce the project during Capstone Days, in English, online or in person.

Support the project

Act as domain expert by refining requirements and giving feedback as the work develops.

Student delivery

A student or pair completes the project under faculty supervision.

Please note: the Capstone is not an internship. Companies are not required to provide a workplace, and the faculty tutor is responsible for the final evaluation.

What makes a strong proposal?

Students most often choose proposals that make the task and the data easy to understand. Every eligible use-case must provide data for the student to work with.

1. A clear task

What question or problem should be addressed? What would count as a successful result?

2. Concrete data details

Describe the format, approximate size, access and availability, constraints, and anything already known.

3. A right-sized outcome

A prototype, analysis, model, dashboard, recommendation, metric, or decision-support result suited to about 150 hours.

What can students contribute?

The Master's program offers two distinct curricula with a shared data-science foundation:

Machine-learning-oriented AI: natural language processing, recommender systems, deep learning, computer vision, time series, and large language models with information retrieval.
Data management for AI: data preparation and structuring, data semantics, data integration, data profiling, semantic understanding, and process mining.

A substantial number of free-choice credits helps students bridge the two paths and develop a broad view of the full AI pipeline. Suitable application domains include bioinformatics, sensor systems, IoT, business information systems, tourism, agriculture, and many others.

For full details on courses and student competencies, see the MSc in Computing for Data Science study plan.

Propose a project or ask a question

If your organization has a suitable data challenge, please get in touch with Dr. Davide Lanti as soon as possible. A short initial description is enough to start the conversation.

Contact Davide Lanti

[1] Tenhunen, S., Männistö, T., Luukkainen, M., & Ihantola, P. (2023). A systematic literature review of capstone courses in software engineering. arXiv preprint arXiv:2301.03554.