Despite its recent gain in popularity, big data is still relatively uncertain compared to other established tech fields. As a result, most beginners find practicing and studying theories and concepts difficult because of a lack of data and resources. However, by using Kaggle for data science, you can overcome this problem with little to no stress.

So, what is Kaggle, and how can you become a professional developer on this platform? Here, you’ll get an overview of this outstanding data science tool and understand why many professionals spend hours on it. Keep reading to discover more.

What Is Kaggle?

Kaggle is an online community for data science and machine learning (ML) enthusiasts. It is a top learning tool for novices and pros, with realistic practice problems to sharpen your data science skills.

Owned by Google, it is currently the world’s largest crowdsourced web platform for data scientists and ML practitioners. Thus, Kaggle gives you access to several professionals in your field that you can brainstorm, compete, and solve real-life problems with.

Why Use Kaggle for Data Science?

With the rise of big data, there are unique ways to source data besides Kaggle, and these methods increase in leaps and bounds daily. There are also various platforms for learning and studying code. So, why should you make Kaggle your go-to learning platform?

In this section, we’ll see the in-depth benefits of Kaggle and what makes it extremely popular with data scientists around the globe.

1. Availability of Datasets

Kaggle Datasets

Kaggle’s datasets are its most used feature, as sourcing real-time data is a significant problem for most data scientists. Imagine spending your time and money learning theories and not being able to practice while you learn.

Kaggle solves this frustrating problem by providing over 50,000 datasets you can utilize while training models. Irrespective of the field you want to work on or the problem you want to solve, there’s a dataset for you on Kaggle.

Of course, working on “hotter” datasets may be more beneficial for a beginner. Although you can apply your knowledge to any problem, it’s easier to get help with more common datasets. Also, note that these datasets come in various file formats, including CSV, JSON, SQLite, and many more.

2. Countless Code Examples

Similar to other developer platforms, Kaggle offers a vast number of code snippets and examples for learning purposes. Studying code from experts is one amazing way to grow as a developer, and yes, data scientists need to write code occasionally.

Like datasets, beginners are better off working with Python for the sufficient availability of code examples, as it is the most popular programming language for data science. Still, for the more advanced learners, Kaggle has code snippets in R, Julia, and SQLite.

More importantly, Kaggle presents these code snippets in a customizable Jupyter Notebook format, allowing you to edit files and make desired changes to your notebook.

3. Targeted Data Science Courses

Although data science is simpler than most people realize, there are a few undoubtedly complex theories in this field. But, for better understanding, there are numerous Kaggle courses on data science concepts, with an emphasis on their practical applications.

Thankfully, these courses are free and come complete with recognized certifications. Moreover, if you’d rather avoid the packed month-long courses available on e-learning platforms, explore these shorter, more direct options.

4. Community

Kaggle discussions

In the tech space, communities are essential for growth and visibility. Kaggle is your one-stop online community as a data scientist, as it gives you the opportunity to learn from others, network, and display your work. You can ask questions, connect with peers, and build on your existing knowledge through your community.

Showcasing your work also helps you build a notable presence as an expert in your field, which is crucial for job hunting.

5. Competition and Motivation

Competitions enable you to see firsthand how you perform against others and how much experience you’ve accumulated. Also, the more tests you successfully pass, the more confident you become in your data science journey.

On Kaggle, there are several data science competitions to test your knowledge against peers and boost your resume. Even better, many of these tests have cash prizes, making them all the more appealing.

Kaggle Pricing

Now, to the most pressing question, "How much does Kaggle cost?" Surprisingly, this data science gem is absolutely free! You can get a range of datasets, engage in competitions, study code samples, and display your work at zero cost. You can sign up at Kaggle.com and create an account to get started.

What Can You Do with Kaggle as a Data Scientist?

As a data scientist, your job involves sourcing and analyzing data. Kaggle presents you with quality data for training AI models and allows you to publish your data findings for public use.

Additionally, you can work with fellow data engineers to solve world problems, build your resume, and get high-paying jobs through constant community building.

How to Use Kaggle for Data Science

After signing up, what next? There are certain steps you should take to get the most use out of Kaggle and further your career while learning.

Like any other learning and community platform, Kaggle can help you get to the top of your game, but only if you know how to maximize its benefits. Here’s a step-by-step guide for you.

1. Get Fundamental Knowledge

Person programming beside a Python textbook

Using Kaggle without basic data science knowledge is equivalent to taking advanced exams without going through your fundamental classes. Yes, anyone can use Kaggle, beginner or not, but you must be grounded in the essential data science concepts in order to avoid confusion.

You need to know how to start your data science career and take some in-depth courses before getting on Kaggle. Also, ensure you understand basic Python programming, statistics, and how to use libraries.

2. Go Through the Datasets

When you’ve successfully acquired beginner knowledge, you can now launch your hunt for data to help you practice. This is where Kaggle’s datasets become helpful to you.

Explore the datasets available, starting from straightforward collections before graduating to more complex ones. Although Kaggle’s datasets are standard, you may still want to carry out checks to ensure the data meets your specifications.

3. Compare the EDA Code Snippets With Your Work

As previously emphasized, studying sample codes is a foolproof way to improve your abilities. Click the Notebooks tab of your selected dataset for code snippets to study and compare with your original work.

Furthermore, focus on code samples with the most activity or from recognized contributors for your exploratory data analysis. This doesn’t imply that other code samples are automatically bad, but chances are the higher the activity, the more accurate it is.

4. Examine the Data Science Notebooks

Correcting your work with code snippets will no doubt improve your abilities with time, meaning you can now advance to more sophisticated challenges. Carefully study notebooks that solve specific problems and try to replicate them.

Note that understanding the methodology and concept will be more beneficial to you than just copying code. While this may boost your visibility, it ultimately won’t make you a better data scientist.

5. Engage in Competitions to Sharpen Your Skills

Kaggle competitions

After going through all the steps above, you should be ready to engage in major Kaggle competitions. Competing may initially appear terrifying, especially when entering your first, but the more you participate, the more confident you’ll become.

Studying can only take you so far; there are certain concepts and methods that competitions alone can expose you to. Besides, the cash prize attached doesn't hurt.

Become a Better Data Scientist With Kaggle

There’s nothing like a community platform to help you better your skills, especially in a field as vast as data science. Active involvement in communities like Kaggle not only improves your knowledge and expertise, but can also expose you to numerous opportunities, including jobs and internships.