Introducing Kaggle and Other Data Science Competitions
Data science competitions have long been around and they have experienced growing success over time, starting from a niche community of passionate competitors, drawing more and more attention, and reaching a much larger audience of millions of data scientists. As longtime competitors on the most popular data science competition platform, Kaggle, we have witnessed and directly experienced all these changes through the years.
At the moment, if you look for information about Kaggle and other competition platforms, you can easily find a large number of meetups, discussion panels, podcasts, interviews, and even online courses explaining how to win in such competitions (usually telling you to use a variable mixture of grit, computational resources, and time invested). However, apart from the book that you are reading now, you won’t find any structured guides about how to navigate so many data science competitions and how to get the most out of them – not just in terms of score or ranking, but also professional experience.
In this book, instead of just packaging up a few hints about how to win or score highly on Kaggle and other data science competitions, our intention is to present you with a guide on how to compete better on Kaggle and get back the maximum possible from your competition experiences, particularly from the perspective of your professional life. Also accompanying the contents of the book are interviews with Kaggle Masters and Grandmasters. We hope they will offer you some different perspectives and insights on specific aspects of competing on Kaggle, and inspire the way you will test yourself and learn doing competitive data science.
By the end of this book, you’ll have absorbed the knowledge we drew directly from our own experiences, resources, and learnings from competitions, and everything you need to pave a way for yourself to learn and grow, competition after competition.
As a starting point, in this chapter, we will explore how competitive programming evolved into data science competitions, why the Kaggle platform is the most popular site for such competitions, and how it works.
We will cover the following topics:
- The rise of data science competition platforms
- The Common Task Framework paradigm
- The Kaggle platform and some other alternatives
- How a Kaggle competition works: stages, competition types, submission and leaderboard dynamics, computational resources, networking, and more
