Data Science

Presentation covering practical data science with the third sector on behalf of DataKind UK. Showcasing specifically the case study of the Companies We Keep project in partnership with Global Witness.

A practical case study detailing the design and delivery of a data science system tailored for non-governmental organisations facing volatile or undefined computing environments, specifically focusing on forecasting mismatches between tutor availability and student demand.

A plenary panel exploring research software engineering (RSE) career trajectories, compensation comparisons, technical track progression, and platform constraints outside typical academic university groups.

A case study analysis highlighting the building of machine learning capacity in the third sector. Breaks down a low-cost serverless dependency risk model designed for a regional UK food bank.

The Brilliant Club supports less advantaged students to access and succeed in the UK’s most competitive universities. They do this by mobilising the PhD community to support students in schools via their courses and tutoring programme. A challenge they face is being able to anticipate the tutor supply they need to meet the increasing demands of their programmes as they expand nationally. A team of six DataKind UK volunteers worked with The Brilliant Club to develop a way to forecast and visualise the mismatch between tutor supply and demand across the UK. This is a talk about how we collaboratively explored their data and built a valuable, new tool for them and, crucially, how we did so in a flexible, scalable way that provides them with immediate value but also will fit into their future use of digital and cloud-based tools.

DataKind UK is a charity which puts into practice this year’s theme: Data, agent for Change. Datakind’s stated mission is “helps social change organisations use data science to have more of an impact” and they do this by connecting them with some of the UK’s best data scientists for free. Dr Adam Hill talked about the sort of projects that he and others from the University have been involved in.

An executive panel discussion focusing on the strategic alignment, systemic barriers, psychological safety, and organisational transformation necessary when deploying new technologies like artificial intelligence for business and society.

In 2015, after nine years as a research astrophysicist across global labs—including a pivotal stint at Stanford University exposed to Silicon Valley’s machine learning boom—I transitioned into industry data science. In this talk, I share the core lessons learned from this journey. I cover how to successfully navigate the leap from academia, how to stand out to industry recruiters, and what “data science” truly means in practice. Finally, drawing on my extensive experience in pro bono work, I explore how to effectively develop and deliver impactful data science projects for the third sector.

A commercially and civically minded session showing how large-scale network mapping can resolve corporate transparency challenges and enable complex risk screening across legal entities.