Interdisciplinary & Innovation-forward
The Interdisciplinary & Innovation-forward initiative at City St George’s, University of London, School of Science & Technology, Department of Computer Science, promotes collaboration between students, staff, and partner institutions to address real-world, interdisciplinary challenges.
It supports the application of data science, machine learning, and artificial intelligence in practice-oriented settings through activities such as hackathons, visualisation challenges, knowledge-sharing and spark sessions, and incubator projects, enabling participants to develop and evaluate data-driven solutions to complex problems.
Data Science & Entrepreneurship Mentoring Event
18 November 2025 | Bayes Business School
On 18 November 2025, MSc Data Science students joined forces with MSc Entrepreneurship students at Bayes Business School for an interdisciplinary Innovation Challenge delivered as part of the Mentoring programme. Led and organised by Alex Galkin and Stefania Zerbinati, the event provided a dynamic environment for collaboration across disciplines, focused on addressing real-world challenges in areas including health, social impact, agriculture, and finance.
This initiative created a unique opportunity for MSc Data Science students to apply their technical expertise within an entrepreneurial context. Working in interdisciplinary teams, students contributed to the development and evaluation of innovative business ideas, ensuring that proposed solutions were grounded in robust data-driven thinking.
MSc Data Science participants played an important role in shaping team outcomes by:
- Understanding business problems and identifying the data required to address key questions
- Assessing data availability, quality, and usability, and proposing strategies to access or generate relevant data
- Evaluating technical feasibility, including appropriate analytical methods, potential challenges, and resource requirements
- Advising on the realism of proposed solutions from a data-driven perspective, including the viability of predictions, insights, and automation
Impact on Learning and Student Experience
The event demonstrated the value of interdisciplinary collaboration in enhancing both technical and professional skills. Students worked intensively in teams to bridge the gap between data science and entrepreneurship, culminating in presentations that showcased innovative, data-informed solutions.
MSc Data Science students made a significant impact by bringing analytical rigour to early-stage business concepts. Their contributions helped teams better understand what could realistically be achieved through data, strengthening the overall quality and feasibility of proposed ideas.
The experience reinforced key learning outcomes, including problem formulation, critical evaluation of data, and the application of analytical thinking in complex, real-world scenarios. It also highlighted the importance of communication and collaboration across disciplines.
