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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 wonât find any structured guides about how to navigate so many data science competitions and how to get the most out of them â 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, youâll 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
The rise of data science competition platforms
Competitive programming has a long history, starting in the 1970s with the first iterations of the ICPC, the International Collegiate Programming Contest. In the original ICPC, small teams from universities and companies participated in a competition that required solving a series of problems using a computer program (at the beginning, participants coded in FORTRAN). In order to achieve a good final rank, teams had to display good skills in team working, problem solving, and programming.
The experience of participating in the heat of such a competition and the opportunity to stand in a spotlight for recruiting companies provided the students with ample motivation and it made the competition popular for many years. Among ICPC finalists, a few have become renowned: there is Adam DâAngelo, the former CTO of Facebook and founder of Quora, Nikolai Durov, the co-founder of Telegram Messenger, and Matei Zaharia, the creator of Apache Spark. Together with many other professionals, they all share the same experience: having taken part in an ICPC.
After the ICPC, programming competitions flourished, especially after 2000 when remote participation became more feasible, allowing international competitions to run more easily and at a lower cost. The format is similar for most of these competitions: there is a series of problems and you have to code a solution to solve them. The winners are given a prize, but also make themselves known to recruiting companies or simply become famous.
Typically, problems in competitive programming range from combinatorics and number theory to graph theory, algorithmic game theory, computational geometry, string analysis, and data structures. Recently, problems relating to artificial intelligence have successfully emerged, in particular after the launch of the KDD Cup, a contest in knowledge discovery and data mining, held by the Association for Computing Machineryâs (ACMâs) Special Interest Group (SIG) during its annual conference (https://kdd.org/conferences).
The first KDD Cup, held in 1997, involved a problem about direct marketing for lift curve optimization and it started a long series of competitions that continues today. You can find the archives containing datasets, instructions, and winners at https://www.kdd.org/kdd-cup. Here is the latest available at the time of writing: https://ogb.stanford.edu/kddcup2021/. KDD Cups proved quite effective in establishing best practices, with many published papers describing solutions, techniques, and competition dataset sharing, which have been useful for many practitioners for experimentation, education, and benchmarking.
The successful examples of both competitive programming events and the KDD Cup inspired companies (such as Netflix) and entrepreneurs (such as Anthony Goldbloom, the founder of Kaggle) to create the first data science competition platforms, where companies can host data science challenges that are hard to solve and might benefit from crowdsourcing. In fact, given that there is no golden approach that works for all the problems in data science, many problems require a time-consuming approach that can be summed up as try all that you can try.
In fact, in the long run, no algorithm can beat all the others on all problems, as stated by the No Free Lunch theorem by David Wolpert and William Macready. The theorem tells you that each machine learning algorithm performs if and only if its hypothesis space comprises the solution. Consequently, as you cannot know beforehand if a machine learning algorithm can best tackle your problem, you have to try it, testing it directly on your problem before being assured that you are doing the right thing. There are no theoretical shortcuts or other holy grails of machine learning â only empirical experimentation can tell you what works.
For more details, you can look up the No Free Lunch theorem for a theoretical explanation of this practical truth. Here is a complete article from Analytics India Magazine on the topic: https://analyticsindiamag.com/what-are-the-no-free-lunch-theorems-in-data-science/.
Crowdsourcing proves ideal in such conditions where you need to test algorithms and data transformations extensively to find the best possible combinations, but you lack the manpower and computer power for it. Thatâs why, for instance, governments and companies resort to competitions in order to advance in certain fields:
- On the government side, we can quote DARPA and its many competitions surrounding self-driving cars, robotic operations, machine translation, speaker identification, fingerprint recognition, information retrieval, OCR, automatic target recognition, and many others.
- On the business side, we can quote a company such as Netflix, which entrusted the outcome of a competition to improve its algorithm for predicting user movie selection.
The Netflix competition was based on the idea of improving existing collaborative filtering. The purpose of this was simply to predict the potential rating a user would give a film, solely based on the ratings that they gave other films, without knowing specifically who the user was or what the films were. Since no user description or movie title or description were available (all being replaced with identity codes), the competition required entrants to develop smart ways to use the past ratings available. The grand prize of US $1,000,000 was to be awarded only if the solution could improve the existing Netflix algorithm, Cinematch, above a certain threshold.
The competition ran from 2006 to 2009 and saw victory for a team made up of the fusion of many previous competition teams: a team from Commendo Research & Consulting GmbH, Andreas TĂśscher and Michael Jahrer, quite renowned also in Kaggle competitions; two researchers from AT&T Labs; and two others from Yahoo!. In the end, winning the competition required so much computational power and the ensembling of different solut...
