Kaggle is one of the best platforms for data science enthusiasts. It has become an equally favourite place for rookies as well as professionals to contribute their solutions to the datasets there. I have been contributing code on Kaggle for almost 3 years now, starting from my bachelor’s till date. In this article, I will be sharing my experience and learnings from 3 years of kaggling.
Start small, reach high
Kaggle is a large place, you will find all kinds of problems there and it is common to get overwhelmed as a novice to this website and the field of data science. One thing to keep in mind here is that to aim at starting with basics and then building from there. This will enable better learning and avoid taking big steps without knowledge.
Competitions are fine, once you become an expert
Kaggle competition comes with an attractive price tag and winning them will be a big hit for your profile. But don’t get disheartened if you are not — you are a beginner and you are still learning. If you feel competitions are too vast for you to even attempt, the best would be to start contributing solutions to the datasets which you think you are capable of and then building up from there. Remember, Kaggle is a public place. You will find all sorts of people here — Industry professionals, researchers, PhD Scholars and novices too. So don’t feel down if you don’t fit in competitions because others are way ahead of you, your time is about to come.
Kaggle is not just ML
Kaggle is a great place for data science enthusiasts, but it is not just about fancy Machine Learning. There are a lot of datasets for Exploratory Data Analysis and Feature Engineering. Practice them too. Analyse them and bring out some interesting inferences. Perform some feature engineering and see how deep you can get. It is also a repository for real-world datasets — Which comes way more interesting to analyse.
It is not about just the datasets
You can also network and meet people on Kaggle. It is a great place to network and share ideas and knowledge. Make it to the best use — this will help you in pursuing your data science journey. Networking is a great task especially if you are a beginner or you are in pursuit of learning Data Science. Taking advice and help from industry experts or your colleagues can provide you with a great learning experience.
Do before you peek
The tendency to peek into others’ work when you work on a problem is common stuff but try not to — because if the solution is known, what is the need of any data science or analyst? Try to solve on your own at first (whether it is complete or partial) and then take a look at the notebooks with the most upvotes or popularity. This will give you a better idea for problem-solving as you keep learning from the top notebooks and go up the ladder of your learning.
Stop Comparing
The most rookie mistake is to compare with other kagglers and ending up with the commonly heard “Imposter Syndrome”. Instead of comparing, see where you are lagging, what you didn’t learn and then work for it. Remember, as I mentioned before, it is a public platform, there are people who are way ahead of you.
Thank you for reading!
Useful Links:
- Find my Kaggle Profile: https://www.kaggle.com/vishnu0399/
- Find me on LinkedIn: https://linkedin.com/in/vishnuu0399
- Know more about Me: https://bit.ly/vishnu-u
