Some context about this post: It all started with a LinkedIn message from Phung Cheng Shyong asking whether I would be interested in participating a datathon as he was looking for teammates. As someone who was not from a data science background, the first thoughts were “What’s a datathon?” and “Pfft, are you looking for the right person?”. But on second thoughts, the event is certainly curious and interesting to an outsider. One thing led to another, and there was I, pictured with the rest of team ALSET as above, having endured a 24-hour challenge of brains while battling fatigue over time limitation.
Here are some thoughts and lessons learned from the event:
- To really become proficient in data science, one would need to have hands-on experience trying/working on datasets (be it for work or hobby) – tutorials alone is sorely insufficient. It is when working on datasets, and attempting to find certain insights through executing certain models, that one realises what needs to be done. Team ALSET is grateful for the sole data scientist, Cheng Shyong who has done prior data analytics work, both for work and hobby. But even so, he cannot complete the challenge with his experience alone, which is why…
- Stack Exchange, Kaggle and other knowledge-exchange sites for data science are all-so important, whether it is to use new analytic tools, or as a refresher on the methods and procedures previously learnt. These sites serve as a guide on how the coding work should go about especially for the new tools required, and also serve as a troubleshoot companion when the coding work was found erroneous. From here, I could see why the data science community is quite a close-knit one as a result of the openness in exchanging knowledge.
- A business model that exploits the prediction models is more preferable than a business model that does not. The nature of the event placed emphasis on the business relevance of the data analytics work done, which meant that teams with only technical-heavy people on-board may not necessarily be at advantage if the team falls short in effectively communicating the ways on how the models can be applied or used in a business setting. For this, team ALSET is grateful to have 2 MBA candidates from Asia School of Business, Maksat Amangeldiyev and Saloni Saraogi, to help with the business case portion of the challenge.
- Do not underestimate the power of sleep and naps. From this experience, I can testify that a 3-hour sleep at 4am is barely enough to take through the afternoon, especially for someone who is not a night person nor accustomed to working with less sleep. An advice from a teammate to take a short nap after lunch proved to be effective as an energy recharge to last the rest of the day.
- Keep an open mind, and be optimistic. Our presentation featured a short video showcasing how the proposed solutions of our business model may look like – to pull off this within the limited time frame seemed (from a personal view) impossible at first. However, Maksat and Saloni have leveraged on their resources and connections to turn this into a reality, which goes to show that ideas should not be discounted altogether at first thought. The both of them have also displayed admirable level of optimism and positivity, which was a great driver to push the team to perform even when the prospects of success seemed minute. Perhaps such optimism is one of the crucial things that defines a successful person – one that is able to become the positive energy around others even when the goings get tough.
At the end of the day, I believe that this event has provided more than just mere experience; it has provided the opportunity to meet and know different people, and to learn lessons from them.
(A shout-out to Low Yen Wei for suggesting the takeaways to be written into an article. This article is also published as an article at LinkedIn: https://www.linkedin.com/pulse/from-what-i-did-takeaways-my-first-datathon-data-unchained-yau/ )
Techtopia 