We like to share and engage with wide audiences on our work, knowledge, and opinions on data science, ML/AI, and software development. Here’s a collection of talks we’ve given, with links to slides and recordings where available. If you want us to speak at your event or meetup, or just to have a chat about one of the topics below, drop us an email.
LLM Coding Tools - Share & Learn
Leeds Data Science Meetup, November 2025, Speaker: Dan
LLMs have already substantially changed how many of us write code; Copilot, ChatGPT, Claude, Cursor, Gemini, SWE-agent, Q Developer, Codex—the list goes on.
In this session, I will present my own explorations into some of these tools. Crucially, you’ll all be invited to input and share your own experiences and opinions throughout! By the end of the session, you should have a solid sense of the tooling landscape—and a clearer idea of which tools might be worth your time, and which to skip.
Future of AI (and Humanity?)
On Purpose cohort training workshop, November 2024, London, Speaker: Dan
AI is reshaping tech, business, and society—but it’s hard to separate real impact from the hype and mysticism spun by companies and pundits.
This session will cut through the mysticism, equipping you to engage critically and productively with AI, machine learning, and data science. Then we’ll use this knowledge to explore the future of AI, and its potential to be an entirely paradigm shifting technology for humanity, the possible benefits, but also the ethical and even existential challenges.
Slides: part 1 - On Data Science, part 2 - More on Data Science, part 3 - Large Language Models, part 4 - An aside on Open Source Software , part 5 - Future of AI (and Humanity?)
Building a minimal data science platform
Data Science Festival - Oktoberfest 2023, October 2023, London, Speaker: Dan
Data science teams often support diverse projects and requests within their organization. As the team grows and tackles more complex projects, software and infrastructure challenges arise in delivering value efficiently and maintaining it in the long run.
We will walk through building a data science platform step-by-step, from the ground up, starting with a single data scientist and scaling to a full team handling multiple projects. By zeroing in on the emerging pain points, we ensure the platform remains lean, addressing only what’s needed, and making every investment in the platform count.
Data science - without the hype (non-technical audience)
Build Peace 2022 conference, November 2022, Leipzig, Speaker: Dan
Data is currently one of the most talked about trends in technology, and there’s now a whole industry built on the value of Machine Learning and “AI”.
I will explain what the practice of data science is, grounded in a (hopefully) simple example. The intention is that you will end this talk having gained a conceptual, but usable, understanding of the data science process.
sktime - Easy Benchmarking
sktime developer conference, June 2022, London, Speaker: Dan
Benchmarking - the comparing and validating of estimator performance on a specific predictive task - is critically important to a data science endeavour making effective progress towards being a valuable application.
We present a brief overview of the challenges when doing benchmarking, what problem we were trying to solve within sktime benchmarking specifically, and a solution that aims to use sktime objects and APIs where possible, and be an easy to use benchmarking functionality packaged as part of sktime.
Data science - without the hype (technical audience)
Freeman Clarke’s internal tech summit, May 2021, Online, Speakers: Dan, Will
Data is currently one of the most talked about trends in technology, and there’s now a whole industry built on the value of Machine Learning and “AI”. So why are most businesses yet to utilise or see tangible results from this?
We present a realistic introduction as to why leveraging data is indeed so valuable, remove some of the unhelpful “hype” around the topic, and cover how to start achieving measurable business gains both strategically and practically.