Mastering the Hackathon: A Data Scientist's Winning Strategy
Athithya Balasubramani is a full-time master’s student at Queen Mary University of London. Focused on focused on newer language processing and generator pretext processing.
Fascinated by data, what it can do and how we can forecast, Athithya competed in the AI Hackathon infront of easyJet and JP Morgan.
“I felt the hackathon would be a great networking opportunity given the presence of large global organisations that specialise in the field of AI […] It was also an incredible development experience as I got to learn first-hand what projects people are involved in and what products are currently being built.
It was a fantastic opportunity as I was able to connect with the hackathon leaders there on the day. This gave me a lot of visibility and exposure, so it was an invaluable opportunity for me to showcase additional learning and development I am currently undertaking.
My key skills and strengths lie in machine learning and data science. In the Hackathon we were working in real life challenges that the organisers were facing, so it was interesting to apply my skills to a real business scenario.
I also found ways to solve problems using natural language processing, and how it can be used in this context. This, and with the arrival of Chat Bots and ChatGPT has meant improvements in industries because of this new technology.”
You mentioned meeting with the representatives from easyJet, what do you think potential employees are looking for in terms of skills?
“To become a good data scientist I think we need to understand the ‘what’ - what is the problem that we are going solve? We have to imagine what would be the ideal solution for any challenge as a starting point. I have learnt from the Hackathon it’s important to keep things simple, as there are a lot of tools within data science, and we can’t implement them all to obtain a solution!”
What advice would you give to anyone thinking of taking part in the AI Summit 2023 Hackathon this year?
“My advice is to be prepared. Once you receive the data start the cleaning process as soon as you can, because the time on the day will go very quickly.
Imagine the problem with the data set before you start solving the problem. Ask yourself, what will be the problems we can receive with this data set? What can be the steps you can do once you get the question, and how will you delegate and coordinate within your team?”