Despite its recent gain in popularity, big data is still relatively uncertain compared to other established tech fields.
So, what is Kaggle, and how can you become a professional developer on this platform?
Keep reading to discover more.
What Is Kaggle?
Kaggleis an online community for data science and machine learning (ML) enthusiasts.
Owned by Google, it is currently the worlds largestcrowdsourcedweb platform for data scientists and ML practitioners.
Why Use Kaggle for Data Science?
There are also various platforms for learning and studying code.
So, why should you make Kaggle your go-to learning platform?
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 might utilize while training models.
Of course, working on hotter datasets may be more beneficial for a beginner.
Although you might apply your knowledge to any problem, its easier to get help with more common datasets.
Still, for the more advanced learners, Kaggle has code snippets in R, Julia, and SQLite.
Thankfully, these courses are free and come complete with recognized certifications.
Community
In the tech space, communities are essential for growth and visibility.
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’ve got the option to sign up atKaggle.comand 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.
How to Use Kaggle for Data Science
After signing up, what next?
Heres a step-by-step guide for you.
you oughta knowhow to start your data science careerand take some in-depth courses before getting on Kaggle.
Also, ensure you understand basic Python programming, statistics, and how to use libraries.
This is where Kaggles datasets become helpful to you.
Explore the datasets available, starting from straightforward collections before graduating to more complex ones.
smack 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.
Carefully study notebooks that solve specific problems and have a go at 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 wont make you a better data scientist.
Besides, the cash prize attached doesn’t hurt.
