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In the minor in Data Science & Artificial Intelligence the art of communication meets the science of data.
The minor is designed for the next generation of ‘Data Translators’ - visionary leaders who bridge diverse fields with the technological frontier of AI and Data Science. In this minor, students of Breda University of Applied Sciences (BUas) from other BUas programmes than Data Science & Artificial Intelligence (DS&AI) collaborate with DS&AI students to find solutions to real-life problems of the industry you study for using data and artificial intelligence applications. DS&AI students act as the technological experts, and you, together with other students from your programme, act as the domain experts. Together you will analyse the problem, assess solutions used in the past, describe data needed to contribute to the solution, develop the solution, and present the outcomes to your client.

Examples of potential projects are:

• Diminishing returns of web shops by designing smarter recommender systems;
• Findings smart solutions for last mile distribution;
• Using generative AI in games production;
• Using robotics in hospitality;
• Increasing advertising revenue’s in media by using data in content development;
• Saving energy by analysing data in smart buildings;
• Using data to improve the impact of social media campaigns.
• Using generative AI to improve a tourism policy learning game (scithos and SmartCulTour)
• Curating Geo and Cultural data for a location-based game
• Explore the roles of digital virtual intelligent agents in making citywide intangible and tangible cultural heritage accessible for citizens, visitors and tourists.
• Create visualised virtual agents that represent hidden cultural actors through whom users can discuss heritage sites – either as a ‘tour guide’ or a ‘character’ associated with a specific local place
• AI application in services experiences of museums and cultural institution
• Automation of customised travel: Users input their destinations, number of travelers and travel preferences. Then they can get a “tailor-made” travel plan for a destination
• Using AI in roots tourism, to help reconstruct a network of relationships and family histories, through memory artifacts like photographs, letters, or other family documents or via places that are now abandoned
• AI applications in enhancing cultural, societal, sustainable and the generic customer journey. How can these be implemented into policies and strategies for creating a well-balanced approach to promoting the tourism industry?
• How can AI serve to promote Equality, Diversity and Inclusion and enhance the experiential journey of the 21st- century tourist, and simultaneously assist in creating a sustainable tourism industry? 
• Applying AI to the management of the tourism company/DMO sustainability. Use AI (fed with bid data, scientific principles and policy requirements) to draft fit for purpose and fir for tourism company (DMO)  sustainability strategies; with the help of AI to critically assess to which extent these are realistic within company/DMO context; to propose realistic action plans and business models to run sustainability transitions and to develop monitoring/agile strategy adaptation mechanisms (development of the decision support tools for tourism companies and DMOs).
• Applying AI to identification of sustainability pathways versus greenwashing claims within tourism sector (a very tropical subject in the framework of the discussions about the stricter regulation for the sustainability greenwashing). 
• How to use AI in tourism to better inform (advanced personalisation) and nudge travellers (iteractive crowdsourcing procedures or any other onses) at the earlier stages of decision-making to more sustainable tourism travel choices. This moment is – when tourists only start brainstorming/investigate/browse holiday ideas- before booking any travel mode; accommodation; experiences; etc – help of AI in understanding how to capture that moment and how efficiently nudge tourists into more sustainable tourism travel choices (we can try to facilitate the contacts with Google for this assignment).
• How to use AI in changing existing tourism travel narrative of “further is better” and “faster is better”: this narrative forming starts from the children books; largely communicated via the marketing/publicity; press and policy discourses; social media, etc. Help of AI to identify and analyse all the channels forming this narrative and development of pathways to change it.
• ABEL/AT/AI minor: tourism traffic flow management/policy integration within mobility/logistics policy. At the moment mobility, logistics transport and tourism mobility policy making/target setting are not integrated. AI can help develop and improve the integral policy, targeting emissions reduction (e.g. analysing effects of the modal shift in tourism transport and providing more insight into the interaction between passenger, freight transport, tourism travel and infrastructure). This assignment closely relates to NLAIC and is one of its priorities in relation to mobility/logistics.

In this minor you will embark on a transformative journey, where the art of communication meets the science of data. This program is designed for BUas students poised to become the next generation of 'Data Translators'—visionary leaders who bridge diverse fields with the technological frontier of Artificial Intelligence and Data Science.

As a participant, you will:

• Engage in Cross-Disciplinary Collaboration: Unite with DS&AI technologists to tackle real-world industry challenges through the lens of your domain expertise.
• Develop as a Domain Translator: Learn the essential skill of articulating complex industry problems into data-centric language that AI experts can navigate upon and innovate in.
• Innovate with Impact: Generate cutting-edge solutions by synthesizing past approaches, current data insights, and AI applications to elevate your field.
• Deliver Transformational Solutions: Craft and present revolutionary, data-driven outcomes to industry clients, steering your sector toward a smarter, more efficient future.
• Through hands-on projects, you will not only contribute domain knowledge but also become fluent in the strategies involving data analysis and AI application. This minor is not just about finding answers—it’s about pioneering them.

Projects could include:

• Revolutionizing E-commerce: Design advanced recommender systems to minimise returns and enhance the shopping experience.
• Innovating Distribution: Develop intelligent solutions for 'last mile' delivery challenges.
• Advancing Game Production: Integrate generative AI to push the boundaries of creativity and personalization.
• Transforming Hospitality: Employ robotics to revolutionise customer service and operational efficiency.
• Elevating Media Revenues: Utilize data-driven content strategies to amplify advertising impact.
• Optimizing Energy Efficiency: Analyze smart building data to significantly reduce carbon footprints.
• Enhancing Social Engagement: Leverage analytics to maximize the influence and reach of social media initiatives.

At least 90 credits in the post-propaedeutic stage at the start of the project. 

You can only participate in this minor if you are enrolled in one of the following bachelor’s programmes: Built Environment, Creative Business, Creative Media and Game Technologies, Hotel Management, International Facility Management, Tourism Management, Leisure & Events Management, Logistics Engineering, or Logistics Management.

All materials will be distributed via the Digital Learning Environment of DS&AI (Brightspace)

Individual contribution to the project will be recorded in a worklog and a learning log. This individual contribution will be assessed via a rubric that is being presented at the beginning of the project.

Topics

Foundations of Artificial Intelligence

• Comprehensive introduction to AI concepts, history, and applications.
• Exploration of various AI disciplines: Machine Learning, Deep Learning, Neural Networks, NLP, and Robotics.

Ethics, Law, and Data Governance

• In-depth discussion on data privacy, intellectual property, and legal frameworks.
• Ethical considerations in AI: bias, fairness, transparency, and accountability.
• Global and regional data protection regulations (e.g., GDPR, HIPAA).

Research Methods and Problem-Solving

• Strategies for identifying research questions in AI and data science.
• Qualitative and quantitative research methods.
• Problem-solving frameworks and critical thinking skills for 'wicked problems'.

Communication and Data Storytelling

• Techniques for effective data visualisation and presentation.
• The art of storytelling with data to influence decision-making.
• Communication skills tailored for diverse audiences, including technical and non-technical stakeholders.

Driving Change in Organizations

• Theories and models of change management in the context of technological innovation.
• Case studies on organisational adaptation to AI-driven workflows.
• Strategies for overcoming resistance and managing stakeholder expectations.

Project Management in Tech-Driven Environments

• Fundamentals of project management: planning, execution, monitoring, and closure.
• Agile methodologies and their application in AI projects.
• Collaboration tools and techniques for managing interdisciplinary teams.

Structure of minor
This minor works with a project-based approach, supported by an extensive digital learning environment, which students use to acquire the knowledge and skills used. During lab days students work in groups of approx. six(50% domain experts, 50% data specialists on a real life project, for a real life client under supervision of experienced staff.