Data & Design Hackathon

I took part in the Data & Design Hackathon on 30 March 2026, where participants worked in teams to analyse data, identify patterns and present practical insights within a limited timeframe. Our project focused on understanding how energy inefficiency overlapped with socio-economic disadvantage across Sheffield.

As someone from an education background, working with data felt unfamiliar at first. While some team members used Python and R, I focused on using Excel and pivot tables to organise, clean and interpret the data step by step. This helped me contribute to the team’s analysis and understand how clearer data structures could support better insight generation.

The project gave me hands-on experience in data cleaning, pattern recognition, visual communication and teamwork under pressure. It also helped me understand that data analysis is not only about using advanced tools, but about asking clear questions, organising information carefully and making findings understandable for others.

What I Did

During the hackathon, I contributed to:

• reviewing the project brief and understanding the problem context
• organising and cleaning data using Excel
• using pivot tables to explore patterns in the dataset
• identifying links between energy inefficiency and socio-economic disadvantage across Sheffield
• discussing findings with team members from different technical backgrounds
• supporting the development of visual outputs, including the dashboard, model and poster
• contributing to the final presentation of the project within a short deadline

Outcomes

The hackathon resulted in a set of visual and analytical outputs, including a data model, an interactive dashboard and a final poster presenting the key findings. Through the analysis, our team identified patterns showing where energy inefficiency appeared to overlap with socio-economic disadvantage across Sheffield.

The project helped translate a complex dataset into clearer insights that could be communicated to a wider audience. It also demonstrated how simple analytical tools, such as Excel and pivot tables, can support meaningful data interpretation when the problem is well structured.

Skills Developed

This experience strengthened my ability to:

• work with unfamiliar and messy data
• use Excel and pivot tables for basic data analysis
• identify patterns and relationships within a dataset
• translate data findings into clear visual and written outputs
• collaborate with people using different technical tools and approaches
• work effectively under time pressure
• focus on clarity before complexity when solving a problem