
About Skills Gap
Skills Gap is a career accelerator that runs insightful workshops for young people aged 15-19 – because it’s never too soon to think about building your skills for a competitive edge in your future studies and career.
We're offering a range of intensive workshops this autumn, all designed specifically for 15-19 year olds and delivered to small groups by experienced industry experts:
Coding for Game Design
Saturday 22 October
With one third of the world’s population already estimated to be gamers, the gaming industry offers one of the hottest career tickets today. Our online workshop gives you a unique insight into the latest trends from multiplayer games to game streaming. Led by Josh Hills, a programmer on some of the world’s largest multi-player games, including Runescape and Horizon, you’ll get first-hand experience of developing and enhancing a physics-based game prototype and discover the skills you’ll need to succeed.
AI in Engineering
Saturday 5 November
Finding new and optimal ways of doing things is at the very core of engineering. Which makes it the perfect partner for AI and machine learning, from smart production lines to image processing technology that allows machines to see. Our online workshop is led by civil engineer and data scientist, Johan Hagstrom, who will introduce you to key AI algorithms and trends. You’ll also get hands-on experience with Python, being guided through a computer vision algorithm that can detect cars on the road – a peek into the world of self-driving vehicles.
AI in Finance
Saturday 12 November
The world of finance has been an early adopter of machine learning, from fraud detection to finance chat bots, yet it’s still just touching the surface of AI’s potential. Our workshop is led by Chelsea Murray, a data scientist at ING with a Masters in Machine Learning and Machine Intelligence from University of Cambridge. Chelsea will introduce you to the vast range of financial AI applications and give you practical experience training a machine learning algorithm to predict credit card fraud.