Why is Kaggle so important for a Data Scientist?

The precise reason why Kaggle is important for a successful data scientist is that the data and the business problems presented in Kaggle are real in nature. Although the data provided is cleansed and structured and in no way represent the data in the actual business environment; still candidates are able to know the real-world business problems that organizations are facing; and how to approach them basis the underlying data. This is more so relevant as the data collection, cleansing and preparation are generally not done by data scientists in most of the organizations.

Kaggle is a platform where we can do live projects and analyse ourself by seeing our ranking.
It gives us Rank on the basis of Accuracy we scored.
It is important for Data Scientist guy may be the reason could be here we get to deal with everything which we learned in Data Science and using that whole concept how to predict something with higher accuracies.

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Primarily the following reasons:

  1. Interesting and challenging projects where contributors can learn and practice
    Kaggle competitions involve solving challenging and interesting problems. Companies post projects to numerous contributors. It especially a great place for beginners who are just trying to break into the data science field. Aside from the competitions that are open to the general public, Kaggle also has private competitions which are only open to top-rated participants (Kaggle Masters).

  2. Insightful discussions with industry leaders and learned experts
    Apart from the projects, Kaggle also consists of live discussions between numerous people on the platform. Such forums are very interesting, stimulating and informative. Through these discussions, you can either seek advice from others or offer advice to people who are dealing with issues you understand

  3. Kaggle offers its audience a chance to get into the biggest data science community in the world
    This platform is trusted by some of the largest data science companies of the world such as Walmart, Facebook and Winton Capital. On Kaggle, data scientists get exposure and a chance to work on problems faced by big companies in real-time. While it is not a guarantee, there is always the chance that the company will be impressed enough to recruit.

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Kaggle offers plenty of resources for aspiring to advanced data scientists. Benefits of this website include, but are not limited to: data, code, community, inspiration, competitions, and courses.

The fact is that despite the concerns Kaggle was never intended to copy machine learning and data science in the real world. If someone is looking for extensive exposure to different types of data and feature engineering techniques, learning how to iterate model building more quickly, and be connected to a remarkable community of data scientists, then Kaggle is a great learning place.

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As you may know, Kaggle is the world’s largest data science community with bunch of tools and resources to help you achieve your data science goals. At best, you can use the datasets provided on Kaggle to build your own Machine learning models.

kaggle is a well-known platform that allows users to participate in predictive modeling competitions, to explore and publish data sets and also to get access to training accelerators. It’s a great ecosystem to engage, connect, and collaborate with other data scientists to build amazing machine learning models.
Over the years, Kaggle has gained popularity by running competitions that range from fun brain exercises to commercial contests that award monetary prizes and rank participants. Participating in these competitions can also open the door to recruitment by top firms. A lot of companies that are bogged down by tough data science problems or lack an in-house team look to Kaggle contests to fill that void.
Without a doubt, Kaggle is the largest online community for data scientists. For beginners looking to embark on their journey in the field, Kaggle is a valuable platform to get started and build a shining portfolio.
But should an aspiring data scientist rely solely on Kaggle to get a foot in the industry? Do data scientists need to keep Kaggling?
Personally, I believe that data scientists shouldn’t use Kaggle as a yardstick. In fact, aside from educational purposes and its usefulness in discovering data sets, I prefer to stay away from Kaggle contests completely. There are two major reasons why I feel it’s not worth the time for aspiring data scientists in the long run.

Surely kaggle is a wonderful website, moreover I would recommend you to spend some time on the micro courses for better understanding.