You can’t improve unless you get opinions or honest feedback from others, and we can’t expect honest feedback from our loved ones because they value our feelings.
But what if I tell you that there exists a platform where you can get judged by many professionals and beginners and also get rewarding opportunities, that is Kaggle. It is specifically made for data scientists. It wouldn’t be wrong to call it a ‘Data Scientists’ World’, because you’ll be able to share your work with the community where you’ll find nothing but data scientists, everywhere.
They will give you honest feedback on your work so that you can improve and get better in your field. Not just that, by being active there, you’ll build your own portfolio which will show your excellence and in this way, you will also get work opportunities there.
Now, the benefits of Kaggle don’t end here. I’ve got a lot to talk about, so why not jump in the post to know more about it? Yes? Great, let’s dive in!
Why Use Kaggle for Data Science? [5 Reasons]
With the rise of big data, there are many unique ways to source data besides Kaggle, and these methods are increasing rapidly. There are also various platforms where you can study and learn coding. So, why should Kaggle be your go-to learning platform?
In this section, I will discuss in-depth the benefits of Kaggle and the reasons that make it extremely famous with data scientists around the globe.
#1 Reason: Availability of Datasets
Kaggle’s datasets are one of the most used features, as sourcing real-time data is a huge problem for most data scientists. Imagine spending your money and time learning theories and not being able to practice while learning.
Kaggle solves this problem by providing over 50 thousand datasets that you can use while training models. Irrespective of the problem you want to solve or the field you want to work on, there is a dataset for you on Kaggle.
Working on “hotter” datasets, of course, may be more useful for a beginner. Although you can apply your learning to any problem, it’s easier to get aid with more common datasets. Also, note that these datasets come in many file formats, including SQLite, JSON, CSV, and many more.
#2 Reason: Customizable Code Snippets
Learning code from experts is a treat for all data scientists because their job requires a big portion of coding. And guess what, Kaggle offers a vast number of examples and code snippets for learning purposes just like many other developer platforms.
Now, most of the data scientists at the initial stage consider working with Python because its code examples are easier to find. But if you are an expert data scientist and you also work with R, Julia and SQLite, don’t worry, Kaggle has got your back.
It provides code snippets for all these languages in a customizable Jupyter Notebook format, so you’ll get the perk to edit them anytime and can alter them because it will be your own notebook and you’ll be the one to decide whether to keep a code or not.
#3 Reason: Targeted Data Science Courses
If you are like me who gets really bored while watching hours-long video courses, you must consider watching Kaggle’s short courses.
Because, unlike the other courses, they just cover the most important data science topics that are crucial to understand. Many YouTube gurus have made data science a complex subject, while it is not. You just need to know the basics of it and then you’ll automatically go with the flow.
Kaggle makes sure that you get an understanding of each topic without getting bored. Now, you might be wondering about the cost of each course, let me assure you that each data science course in Kaggle is completely free of cost. Get a certificate of completion without spending a penny.
#4 Reason: Community
I know criticism can be toxic sometimes, but it is necessary. You see in a democratic state, people, journalists and opposition parties are allowed to criticize the prime minister, ever wondered why?
Because that criticism allows the prime minister to see what he really is, whether he is going in the right direction or not, whether he is doing good for the country or not.
So just like politics, as a data scientist, you’ll also need someone who can judge your work and criticize it honestly, so that you can see whether you’ve done it correctly or not.
Only accept criticism from the people who you want to be in the future. And on Kaggle, you’ll find a lot of Data scientists, who’ll not just judge your work but will also give you additional tips and tricks that you’ll never find in a college or a university.
Not only that, it would be the biggest perk for you to network with other data scientists, your chances of getting a good client will increase 10x more.
#5 Reason: Competition and Motivation
You know your expertise only when you compete with a person better than you. And as Kaggle wants you to become a professional data scientist, it provides you with a platform where you can take part in competitions.
If you lose the competition, it will boost you to improve your skills and do the best next time, and if you win the competition, it will make you more confident about your skills and you’ll never undermine your skill set while applying to a big job.
How to become a Data Scientist?
Becoming a data scientist is not as hard as you think it is. You just need to take a credible data science course and a lot of practice to polish your skills. (A degree in Computer science would be preferable).
Why you should take the Data Science Course in Delhi NCR?
Above I said, you need to take a ‘credible’ data science course to become a data scientist, why? Because credibility plays the most important role when you’re looking for a course to learn and acquire efficient skills.
In my opinion, there is no better course than the data science course in Delhi NCR. New companies are emerging in Delhi at a fast pace, and becoming a data scientist there would be a great option. With the right skills, you’ll be able to win the job in your city.
Bottom Line
The main crux of this post is that you should look for only credible platforms where you can either become a data scientist or you can build your data science skills.
Kaggle should be your priority, and secondly, if you’re taking data science seriously, you should consider taking a data science course.
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