What Is Kaggle and What Is It Used For?

Kaggle is a platform for data science competitions where participants compete to create the best models for solving specific problems or analysing certain data sets. The platform is also used for learning, collaboration, job opportunities, community building, and research in the data science and machine learning fields.

Kaggle is a valuable resource for data scientists and machine learning engineers looking to improve their skills, collaborate with others, and tackle real-world data problems. Discover more about  Kaggle, how it is used, and what the competitions are like.

What is Kaggle?

Kaggle is a platform for data science competitions, where data scientists and machine learning engineers can compete to create the best models for solving specific problems or analysing certain data sets. The platform also provides a community where users can collaborate on projects, share code and data sets, and learn from each other's work. Founded in 2010, Google acquired Kaggle in 2017, and the platform is now part of Google Cloud.

Kaggle hosts various competitions sponsored by organisations, from predicting medical outcomes to classifying images or identifying fraudulent transactions. Participants can submit their models and see how they perform on a public leaderboard and receive feedback from other competitors and the community.

In addition to competitions, Kaggle offers public data sets, machine learning notebooks, and tutorials to help users learn and practice their data science and machine learning skills. It has become a popular platform for novice and experienced data scientists to improve their skills, build their portfolios, and connect with others in the industry.

What is Kaggle used for?

Kaggle is primarily used for data science competitions, where participants can compete with each other to create the best models for solving specific problems. Organisations from around the world sponsor these competitions, and they cover a wide range of topics, such as image classification, natural language processing, and predictive modelling. 

Kaggle is also used for:

• Learning: Kaggle provides resources such as public data sets, machine learning tutorials, and code notebooks that allow users to learn and practice data science skills.

• Collaboration: Kaggle allows users to form teams and collaborate on submissions, share code and data sets, and provide feedback to each other.

• Community building: Kaggle has a large community of data scientists, machine learning engineers, and data enthusiasts, providing a platform for users to connect, share ideas, and collaborate on projects.

• Research: Kaggle's datasets and competitions are impactful for research purposes, making it a platform for testing and improving machine learning algorithms.

Overall, Kaggle is a versatile platform that offers a range of opportunities for data scientists and machine learning engineers, from learning and collaboration to research.

What are Kaggle competitions?

In kaggle competitions, data scientists and machine learning engineers compete to create the best models for solving specific problems or analysing certain data sets. Various organisations sponsor these competitions, ranging from businesses to academic institutions, and participants worldwide are eligible to compete.

Competitions typically involve a data set and a problem, and participants must develop and submit a model that solves the problem or predicts the target variable with the highest accuracy. Depending on the nature of the data set and the problem being solved, competitions have various structures, such as classification, regression, or computer vision.

Competitors collaborate and share ideas throughout the process, and some competitions even offer prizes to top-performing teams. Competitors can also participate in discussions and forums related to the competition, where they can ask questions, share their progress, and get feedback from other participants.

Kaggle competitions are a great way for data scientists and machine learning engineers to hone their skills, learn new techniques, and solve real-world problems. They offer a platform for collaboration, networking, and career advancement and have become a popular way for organisations to crowdsource solutions in data-driven challenges.

Below are three examples of advanced Kaggle competitions and what their prizes are like:

Vesuvius Challenge: Ink Detection

The grand prize amount for this competition was 700,000 USD for the first-place team, with a 1,000,000+ USD total prize pool. Over 1,249 teams competed in this challenge, which revolves around reading ancient scrolls discovered after hundreds of years [1]. 

Google: Isolated Sign Language Recognition

Google's total prize for this competition was 100,000 USD, with the first-place team taking home 50,000 USD. Over 1,000 teams entered this competition, which aimed to help family members and friends of deaf individuals learn basic signs to communicate effectively [2]. 

Lux AI Season 2

With a total prize pool of 55,000 USD, over 600 teams signed up to compete in this competition to attempt to win the first-place prize of 15,000 USD. The focus of this challenge was on multivariable optimisation and an allocation problem. The competition was also carefully designed to include an element of one-on-one competition against other competitors [3]. 

Kaggle competitions for beginners 

Outside of normal competitions, Kaggle provides beginner-friendly options if you are just starting your journey as a data analyst or data scientist. To find them, you can go to the competition section of the Kaggle website and find the “Get Started” section. In this section, Kaggle tells you that the options displayed suit newcomers without prior experience.

The beginner competitions, however, do not come with cash prizes for winners and have no set deadline for when you or your team has to submit your answer. Instead, beginner competitions include a rolling timeline. You can become familiar with the Kaggle website and how competitions work from these beginner options, and you can begin to meet other people within the community. 

A more detailed description of three beginner competitions follows: 

Titanic: Machine Learning from Disaster

This challenge introduces machine learning while exposing you to how the Kaggle website and competitions work. This competition uses machine learning techniques to predict who would survive the Titanic disaster [4]. 

Housing Prices: Advanced Regression Techniques

The Housing Prices competition is perfect for you if you have some experience with machine learning or data science or if you have utilised R or Python in some capacity before. This competition includes more advanced regression techniques, such as random forest and others [5].

Spaceship Titanic

In this competition, you must use the data science skills you are building to solve a fun mystery. The mystery revolves around missing passengers from an interstellar voyage. Kaggle recommends this competition for beginners to learn basic skills and become familiar with the website [6]. 

Is Kaggle useful for finding employment?

Kaggle can be a useful platform for employment in data science and machine learning. From 2014 to 2022, Kaggle had a dedicated job board for their website users to make it easier for them to find data science jobs. Due to many resources now available for job seekers, Kaggle decided to shut down its job board completely. 

However, many great aspects of Kaggle still exist to help you find employment.  Here are a few ways in which Kaggle can help with job hunting:

• Showcase of skills: Participating in Kaggle competitions can showcase your data science and machine learning skills to potential employers. Winning or placing highly in a competition can demonstrate your abilities to solve real-world problems, work with data, and develop predictive models.

• Networking: Kaggle has a large community of data scientists, machine learning engineers, and data enthusiasts. Participating in competitions, collaborating on projects, and contributing to the community can help you connect with other professionals in the field and potentially lead to job opportunities.

• Learning: Kaggle provides resources such as public data sets, machine learning tutorials, and code notebooks that allow you to learn and practice data science skills. This can help you improve your knowledge and expertise, making you more attractive to potential employers.

Kaggle can be a valuable tool for finding employment in data science and machine learning. By participating in competitions, networking with other professionals, and showcasing your skills, you can increase your chances of finding job opportunities and advancing your career.

Next steps with Kaggle and data analytics

Kaggle is a global community of data science and machine learning professionals that offers competitions and learning opportunities. If you’re interested in learning more about topics related to Kaggle, completing a course or receiving a relevant certificate is a great place to start. 

Check out the Google Data Analytics Professional Certificate to begin your journey in data analytics. This course is for beginners with little prior experience and is entirely online for your convenience as a student. It takes around six months to complete, factoring in under ten hours of studying per week. 

Completing this certificate exposes you to analysing and processing data effectively, utilising R programming for your benefit, using key analysis tools, and creating impactful and informative visualisations to showcase your data.