Form teams, Solve REAL projects and win prizes.
The largest student-organised data science hackathon in the UK. Our flagship AI Hack this year will be a virtual 24h frenzy of hacking and data science. It aims to bring the most forward-thinking and creative student data scientist to solve some of the world's most pressing challenges. Joining us for the hackathon will be a superb experience to:
- Test your data science skills and to apply them to real projects
- Chance to level up your CV for the career goals you aspire towards!
- The experience of obtaining professional mentorship during our event
£2,060 in prizes
First place for Each Challenge (3)
Second Place for Each Challenge (3)
Third Place for Each Challenge (3)
Social Media Challenge (2)
Kahoot Data Science Quiz Challenge (2)
Submitting to this hackathon could earn you:
Teams that already have registered with us are eligible to participate in AIHack 2021. Do not sign up if you do not have a confirmation e-mail from us already.
You must submit your code and a video presentation where you clearly explain what you have done on Devpost for judging. The judging criteria is enclosed and you may find this useful!
No hard requirement on the length of the written report. E.g. the main text can be 2-3 pages, but you should stop once you think you have elaborated your analysis enough. A suggestion is that it should have i) a non-technical summaries of your key findings and their significance, and ii) some technical exposition to describe your exploratory analysis/data processing/modelling. Supplementary plots, tables etc can be but into an appendix, if needed.
Imperial College London
Originality of angle of exploration (Interesting questions answered, use of valid alternative datasets)
Data Exploration 
Quality of techniques used to pre-processed data and to give valuable insights about the dataset(s)
Insight Visualisation 
Quality, relevance and effectiveness of visualisations used for exploration and/or analysis
Analytical Techniques 
Sophistication and correctness of methods of analysis. Cannot score high if cannot justify method.
Model Validation 
Use of metrics in showing performance of analysis.
Interpreting the Result 
Ability to interpret the result of the analysis and take a step back to explain the bigger picture. Ability to make a data-driven "business" decision.
- Machine Learning/AI