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How To Ace Data Science Hackathons

How To Ace Data Science Hackathons

  • We touch upon popular hacks to win data science hackathons.
How To Ace Data Science Hackathons

Data science hackathons can be difficult to crack, especially for beginners. The common misconception is you have to be an expert to participate in data science hackathons. On the contrary, hackathons are a great way to hone your data science skills.

Platforms like Kaggle, MachineHack are helping companies to hire the best data science talent using the hackathon model. Last month, MATHCO.thon concluded its ‘car price predictions’ challenge.

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As many as 2,383 data science professionals participated, of which 50 made the cut. The top three in the leaderboard walked away with cash prizes.  

N Sai Sandeep, had participated in MachineHack: Great Indian Hiring Hackathon last year, for the first time. Unfortunately, he could not perform well. “I applied my learning in the ‘car price predictions’ challenge and got into the top three,” said Sandeep. Other winners, Akash Gupta and Sai Deepak, said MachineHack features like practice and Bootcamp helped them prepare for MATHCO.thon. 

IIT Madras professor Chandrasekharan Rajendran said candidates should continually update their knowledge and differentiate themselves by actively participating in international hackathons. “This should be the benchmark to know where you stand vis-a-vis best in the field,” he added. 

In this article, we touch upon popular hacks to win data science hackathons.

Baby steps

While there is nothing wrong with shooting for the top prize, participants have a tendency to miss the forest for trees. Only the top rung in the leaderboard makes the podium. If you are going in with a win at any cost mindset, you probably end up disappointed. Hence, it’s important to focus on small wins and treat hackathons as learning curves.

“The first step is to start small. And so when you go in dreaming big, it’s hard to complete every single task you set yourself out to do. So instead, ask yourself these questions. What do I start with first? What would produce the best idea in the limited amount of time? What do I want to get at?. These three questions cover most bases,”said Leon Yuan, the winner of TD 2019. “What do you really want to learn? What do you really want to get out of it? are also important questions to consider,” he added.

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Trust the process

“Often, when we talk about data science projects, nobody seems to be able to come up with a solid explanation of how the entire process goes. From gathering the data, all the way up to the analysis and presenting the results,” said data science learning platform,” LEAD’s founder Cher Han Lau.

In his blog, he detailed the OSEMN framework, which covers every step of the data science project lifecycle, from obtaining, scrubbing, exploring, modelling and interpreting data. 

(Source: LEAD)

Tips from hackathon winners 

  • No one at the hackathon is a pro; everyone is experimenting. 
  • You need to have a demo or minimum viable product to win.
  • For machine learning challenges, 90 percent accuracy or higher is ‘nice to have’.
  • Spend time on studying data
  • Communicate blockers with your team/mentor early. 
  • Use virtual communication platforms at all times for maintaining collaborative workflow. 
  • Use Google Colabs or Jupyter Notebook.
  • Make use of visualisation libraries such as Matplot 
  • Sell the business solution. 
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