Kaggle is one of the widely used platforms by data scientists to learn new techniques, compete in competitions, and showcase their skills. For any beginners or practitioners, Kaggle is the go-to platform for advancing their knowledge as well as engaging with other like-minded people through discussions and more. Millions of people from around the world actively use Kaggle for different purposes — learn, compete, and engage. Data science aspirants and practitioners try their hands on numerous competitions, however, only a few people progress in their Kaggle journey as the competition is fierce. Today, there are only 180 Grandmasters, 1,417 Masters, 5,478 Experts, and 54,334 are Contributors on the platform.
Kaggle for aspirants can be overwhelming, which demotivates them as they start evaluating themselves based on what experts are able to deliver on the platform. However, if aspirants can follow best practices and become persistent, they can make the most out of the platform. But, there is no shortcut, it is believed that grandmasters usually spend 2 years on Kaggle continuously engaging to increase their ranking from novice to grandmasters.
Here are some of the effective practices that you can follow: –
Learn The Necessary Data Science Skills
To begin, aspirants should start exploring various sections of the platform such as data, notebooks, discuss, courses, and compete. You can start with honing up your skills by learning through various free courses on the platform. One can even learn right from the basics to intermediate and advanced data science skills. The platform has courses not only on Python, SQL, and machine learning but also explainability and game AI. Obtaining relevant skills is essential to begin your journey on Kaggle.
Engage
One of the best ways to feel comfortable is to engage in various discussions, it will make you believe that you are a part of the community. People here not only discuss data science techniques but also share their experiences on winning the first medal, what practices helped them, among others. Engaging is essential, especially for beginners, as they can feel at home on the highly competitive platform.
Gradually, you should look at others kernels and assimilate their approaches to solving certain problems. You will not always understand why a particular technique was used, but you can comment on kernels and ask users about their approaches.
Start Competing
Directly getting into the live competitions is something you should not adopt as it can be overwhelming. Aspirants should start with solving previous competitions’ problems then compare solutions with the winners’ kernel to analyse the difference in approaches and techniques. “I used to solve previous Kaggle competitions, check the successful solutions and get to the bottom of the approaches with the help of Google,” said Abhishek Thakur, the first 4x grandmaster.
Ability to unlearn and relearn is essential during this stage of your journey on Kaggle. It is not just about the skills but motivation to keep going is vital. To keep your enthusiasm, you can apply these learning on other hackathons hosted on platforms like MachineHack. Weekly competitions are crucial to keep you motivated while continuously learning, as it helps you in evaluating your progress.
Eventually, you can start competing in the live competition hosted on Kaggle. It is recommended to get a team rather than trying individually. Mathurin, Kaggle top 20 ranked master, believes that most of the top medals were won as a team when he was early in his journey on Kaggle participating solo in numerous competitions.
You should focus on building a team of experts with diverse knowledge to increase your probability of success in the competitions. Not only during your initial competitions but also as you progress, a good team would be vital throughout your journey on one of the most used data science platforms.
Outlook
Irrespective to how you plan your journey, you will always hit roadblocks that would impede your progress. However, being persistent at the same time, continuously improving your expertise would make all the difference. One should try to win competitions on Kaggle, but it is not only about coming out victorious — ameliorating your skills and contributing to the data science community are equally important.
