Edinburgh Data Science Meetup

Edinburgh's community meetup for people working in or simply interested in Data Science. We're a friendly mix of professionals, academics, students, and enthusiasts who enjoy meeting over drinks and pizza while listening to insightful talks.

By popular demand, you can now RSVP either on Meetup or via Luma!

We have two great talks lined up:

Stelios Christodoulou Scraping Wikipedia in 2026

This talk will explore the impact of increased web scraping and bot-generated traffic on Wikipedia, along with the bot-blocking and throttling measures introduced in response. Through practical Python examples, Stelios will demonstrate common scraping techniques, discuss potential workarounds, and highlight challenges associated with scraped data. The talk will conclude with a proposed code of conduct for web scraping, informed by guidance from Creative Commons and UK copyright exceptions.

Samuel Agbede Cache Me If You Can

LLM applications are expensive, and many of them are paying for the same answer twice: sometimes ten times. This talk explores how to stop doing that.
Caching is the mechanism. The catch is that "the same answer" stops being obvious the moment your inputs are natural language. "When are your opening hours?" and "What time do you open?" are the same question to a user, but different strings to a cache. The interesting work lies in teaching systems when two questions should be considered equivalent, turning caching into a calibration problem with real cost and quality implications.

Schedule

5:30pm – 6:30pm: Networking with pizza and drinks
6:30pm – 8:00pm: Talks and Q&A
8:00pm onwards: Continued networking at a local bar

Where?

37 Holyrood Road, Edinburgh, EH8 8AQ
Paterson's Land, Room 1.26

Due to the popularity of previous events, we can only admit attendees who have RSVP'd via Meetup or Luma.

Speaker Bios

Stelios Christodoulou

Stelios Christodoulou is a Senior Service Performance Engineer at Viasat and a data analyst with over 15 years of experience across telecommunications, aviation, maritime, transport, and geospatial data. His work has spanned satellite communications, GPS interference analysis, network performance monitoring, remote sensing, and data visualisation using Python, SQL, Tableau, and cloud technologies. A regular contributor to Scotland's Python and data communities, Stelios enjoys exploring the practical applications of data engineering, open data, and web scraping in real-world systems.

Samuel Agbede

Samuel Agbede is a Developer Advocate at Redis, where he helps developers build AI systems that work reliably in the real world. His interests include AI agents, retrieval, memory systems, evaluation, and the engineering trade-offs involved in taking LLM applications from prototype to production. Previously an Applied Machine Learning Engineer at JPMorgan Chase, Samuel combines a background in software engineering, machine learning, and developer education to help engineers build reliable, observable, and cost-effective AI solutions. He is also the host of the Stories That Shape Us podcast, where he explores the experiences and decisions that shape people's careers and lives.