Data Science and Analytics

1. Introduction

This year’s competition aims to inspire creativity, problem-solving, and a passion for working with data in young people, encouraging them to consider a future in the dynamic field of Data Science. The key focus is on using data to understand and address real-world challenges, with a particular emphasis on creating a positive social or environmental impact.

2. Why study at The University of Sheffield?

The University of Sheffield has a global reputation for its exceptional teaching, impactful research, international outlook and outstanding student experience.

• Number one in the Russell Group (National Student Survey 2025, 2024 and 2023, based on aggregate responses).
• WhatUni University of the Year 2024 (Whatuni Student Choice Awards 2024).
• Number one Students’ Union in the UK (Whatuni Student Choice Awards 2025, 2024, 2023, 2022, 2020, 2019, 2018 and 2017).
• A top 20 university targeted by employers (The Graduate Market in 2023, High Fliers report).
• Six Nobel Prize winners (Professor Sir Fraser Stoddart, Sir Harry Kroto, FRS, Sir Richard Roberts, FRS, Lord Porter of Luddenham, OM, FRS, Sir Hans Krebs, FRS and Howard Florey, Baron Florey of Adelaide and Marston, FRS).
• A world top-100 university (92nd in the QS World University Rankings 2026)

Why study Data Science at Sheffield?

Our Data Science program is designed to develop ethical data professionals ready to make a positive societal impact. Taught by active researchers and crafted in collaboration with industry experts, the curriculum focuses on more than just technical skills; it teaches you how to responsibly manage complex data, use your findings to make sustainable decisions, and effectively communicate with diverse stakeholders. As well as learning programming, machine learning, artificial intelligence and statistical analysis, you’ll gain a solid foundation in ethics and critical thinking, ensuring you’re uniquely employable and equipped to influence outcomes for the betterment of society.

3. What is Data Science?

Data is everywhere – from the apps on your phone to Netflix’s recommendation systems to climate prediction models, vast amounts of data are being created and used every second to improve the world around us. Data science is the process of extracting meaningful insights and knowledge from this data. It’s about asking the right questions, finding patterns, building predictive models, and ultimately, making better decisions. Data science isn’t just for business. It’s a powerful tool for creating a positive impact in the world. We can use it to:

• Improve healthcare outcomes: Analysing patient data to predict disease outbreaks, personalize treatment plans, and optimize hospital operations.
• Fight climate change: Using satellite data to track deforestation, modeling weather patterns to predict natural disasters, or analysing energy consumption to promote efficiency.
• Enhance public safety: Identifying traffic hotspots to prevent accidents or analysing social media data to understand and respond to community needs during a crisis.
• Promote education: Personalising learning experiences for students based on their performance data or predicting which students may need additional support to succeed.

4. Competition Task

We’re looking for individual students or teams of up to three members to participate.

Imagine you’re a data scientist… This project challenges you to design a data-driven project that addresses a real-world social or environmental problem. You will not be required to analyse any real data; instead, you’ll focus on the planning, methodology, and visualisation aspects of a data science project.

Project Goals

1. Define a Problem and Target Audience: Choose a specific social or environmental issue that can be investigated using data, and decide who you are targeting. The UN’s Sustainable Development Goals are a great source of ideas for this. Examples could include:
   ◎ Addressing traffic congestion in a major city.
   ◎ Improving waste management and recycling rates in a community.
   ◎ Understanding the factors that affect student well-being in schools.
   ◎ Identifying patterns in the spread of misinformation online.
2. Identify Data Sources: For your chosen problem, identify the types of data you would need to collect. Where would this data come from? Who would be the source? How would you gather it? Be creative but make sure that your choices are ethical! It could be data from a government website, social media, sensors, or surveys.
3. Formulate Key Questions: Based on the data you’ve identified, what are the most important questions you would ask to solve the problem? These questions should guide your analysis and lead to clear insights. For example, if you’re tackling traffic, a question might be: “Which intersections have the highest number of accidents, and is there a correlation with the time of day or weather conditions?”
4. Propose a Solution and Visualisations: Based on your hypothetical findings, propose a practical, data-driven solution to your problem. Crucially, show how you would visualise your insights. You can create mock-up charts, graphs, or a dashboard to illustrate your findings and make them easy to understand for your audience. For example, you could draw a bar chart showing the most common types of waste or a map highlighting pollution hotspots.

Design Considerations

Your project should be presented as a report or presentation that addresses the following points:

• Problem Statement: Clearly explain the problem you’re addressing, why it’s important, and how the target stakeholder(s) is/are.
• Data Strategy: Describe the types of data you would need and where you would get them.
• Methodology & Questions: List the key questions you would ask and explain how answering them would help solve the problem.
• Hypothetical Findings & Visualisations: Present your imagined findings and include hand-drawn or digitally created mock-ups of charts, graphs, or dashboards that would communicate these findings effectively.
• Proposed Solution: Detail your data-driven solution. How would it work? What impact could it have?

The most successful projects will demonstrate a clear and logical approach to using data to solve a problem, even without conducting the actual analysis.