Kaggle’s Role in Data Science Community

Kaggle is based on a simple idea – how does one gets the smartest and most innovative data scientists of the world moonlight for free? Simple, by turning data problems into competitions.

The role that Kaggle plays in the data science industry is indispensable. Let’s take a detailed look at the benefit of this data community to contributors and the data science industry as a whole

Kaggle bridges the gap between sources in need and data scientists

Most of the time accessing the best and most brilliant minds is a matter of looking beyond your geographical confines. This is what Kaggle has managed through crowdsourcing. When a company with a problem posts it on this platform, data scientists ranging from statisticians, mathematicians to programmers and computer scientists all over the world are free to take part and make their contributions. With access to unlimited talent and brilliant minds, the problem is approached from different perspectives that in-house data scientist would otherwise never have thought about.

Gives beginners a chance to take part in solving real-world problems

If you are interested in big data analytics, machine learning, and data science, then you must have gone through a series of courses, and you probably have a few degrees under your belt. No amount of education can be compared to real life experience solving real-world problems that make a difference. The best place to start is on Kaggle where you can have a feel of how data science works in the real-world. On this platform, you have the opportunity to hone and develop your skills. Kaggle competitions provide willing learners with a diverse range of challenging problems. The competitive element in addition to a fixed headline not only helps stimulate you mentally but also helps improve coding skills substantially. The prizes are modest considering the amount of work involved. The true prize is not financial benefit but an opportunity to learn.

Kaggle gives you the opportunity to work on diverse machine learning
problems

It takes time and consistent practice to master challenging machine learning problems such as image recognition, forecasting, NLP (natural language processing) and sentiment analysis. Through solving a wide range of problems, you get the chance to understand all problem domains so that you can effectively handle all types of data using the appropriate algorithms.

Kaggle helps develop time management skills and improve efficiency

Since all Kaggle competitions have a strict deadline, you are put in a situation where you have to work and solve problems under pressure. This is helpful in improving your ability to develop creative ideas and testing out different alternative theories as fast as possible. This is an invaluable experience that you cannot get in school or through courses. By working under strict guidelines, you can stay focused on accomplishing a single goal within the shortest time possible. Additionally, taking on different Kaggle competitions within a short period also helps you master more techniques.

A great platform for data scientists to enhance their coding skills

Becoming a master at coding is all about consistent practice, and this is what Kaggle offers you. The competition on this data community puts you in a position where you have to code and re-code a solution with given constraints. This may include making trade-offs between RAM, CPU and even programmer time. To have a chance at being in the top 10 in any competition on Kaggle you need to be creative and learn how to get rid of performance bottlenecks quickly. With consistent practice, turnaround time will improve substantially.

Contributors understand and learn how to code scoring matrices

As you keep competing on Kaggle, you will realize that this platform uses different scoring mechanisms to rank submissions from different contributors. After participating in some Kaggle competitions, a data scientist naturally learns when different scoring metrics are used and why

Kaggle competitions come in handy in sharpening problem areas that
many data scientists deal with

One of the challenges that even the most knowledgeable and experienced data scientist faces are over fitting data. By engaging in these competitions regularly, you will learn cross-validation and how to retrain and resample the model you come up with multiple times. This is one of the best ways to validate and prove that your model is viable and not over-fitting the data provided.

Kaggle contributions are an opportunity for any data scientists to
master dealing with dirty data

Cleaning dirty data is something most data scientists deal with on a daily basis. This is because it is the first step when trying to solve any data problem. When taking part in Kaggle competitions, contributors have to clean and filter data. They also need skills on handling missing values; this is all part of data cleaning which sets the stage for accuracy when modeling a solution. Keep in mind that most competition planners make the challenge harder by including garbage in the data intentionally

Through participating in Kaggle competitions, contributors learn how
to handle massive files

Throughout the training process learners only get to take part in small projects designed to test their skills. On Kaggle they get the rare chance to participate in solving a problem that entails massive file sizes. Real-world problems require the handling of massive file sizes through extracting, slicing, splitting, zipping and sampling. This makes Kaggle an indispensable resource for a data scientist to prepare for real-world issues. When handling Kaggle projects, contributors do not only deal with big data but also learn and explore through extremely supportive Kaggle forums. During and after the competition, there is a forum where all the participants are given a chance to share their work. This gives each contributor the opportunity to understand the problem intimately, this way they get a good idea of the thought process that other contributors went through.

Kaggle has created a way for data scientists to compete against each other on a global platform. This will not only bring out the best in them and but will also make them realize that machine learning entails more than pushing data through a library algorithm.

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