Everything you need to know about Data Science Competitions

Everything you need to know about Data science competition

It would be such a mundane life if not for various kinds of challenges and competitions to prove your prowess in any field. You would just learn some skills and never get to know if you are the best in that. Engaging in any contest not only provides a massive learning opportunity but also brings to light all the gaps and weaknesses in one’s learning journey. This article will provide information about the most popular online competitive machine learning portals. Furthermore, we will see how any Data Science enthusiast can leverage benefits by participating in these competitions.

Various types of Data Science Competitions:

1) Recruitment contests:

Companies design various contests to not only identify the best solution to their existing problem but also aim to recruit the best participant. The top-ranking participants are indeed given a chance to directly appear for an interview with them. Budding Data Scientists must certainly aim for these contests.

2) Sponsored/Featured competitions:

In the same way, many times companies just want a quick solution to a real-time problem they are facing. Lucrative cash prizes are rewarded to the winners.

3) Research problems:

As the name suggests, the problem will be research-oriented where they would expect to see new and interesting approaches to problem-solving. These usually do not have any cash prize or award associated.

4) Beginner / Intermediate / Expert level Data Science contests:

Select and participate in a contest suiting your expertise level.

5) Practice problems:

Similarly, many practice problems are available and open for aspirants to work on.

Popular portals of Data Science Competitions:

1) Kaggle :

This is one of the most popular sites which started in 2010. This site ensures it has everything that can engage, support and challenge any enthusiast. There are over 19000 datasets and 20000 public notebooks to take advantage of. It is prestigious to top a Kaggle competition.

Data Science competitions portal

2) AnalyticsVidhya :

This is one of the most popular Data Science sites created in India. It has contests, conducts Hackathon, annual workshops and a large number of articles to learn from. Moreover, it is popular for its recruitment contests.

3) DrivenData :

If you like to solve real-world problems, this is the place for you. The datasets listed in Driven Data are related to Non-Profits ranging from wildlife preservation to public health. Furthermore, the contests aim at social good in the areas of Education, Health, Public Services, International Development, etc.

Data Science competitions portal

4) Innocentive :

Here is a site which provides exciting real-time problem affecting human life from facilitating access to clean water at a household level to passive solar devices designed to attract & kill malaria-carrying mosquitoes. Pretty interesting for all those who would love to significantly impact society with their knowledge.

5) Codalab :

Codalab is an open-source web-based portal that provides problems currently being researched upon. For example, Liver tumor segmentation challenge, Emotion challenge, etc.

Data Science Competitions portal

Likewise, there are some other sites which can be explored for example CrowdANALYTIXNumerai

Benefits of participating in Data Science Competitions:

1) Participation helps identify knowledge gaps and areas of improvement.

2) Mentioning the competition in your resume helps to attract the recruiters.  These performances notably add strength to your candidature.

3) It helps gain self-confidence in your knowledge and skills.

4) In like manner, it can help you end up with direct recruitment or cash prizes.

Some useful tips about these competitions:

1) Register in all the types of competition portal:

These Data Science portals regularly come up with some kind of competition updates. It is useful to subscribe to their emails and notifications and get updates about the upcoming competitions.

2) Select competitions based on your expertise level:

It is advised that you select the competition problem based on your interest and expertise level you find yourself fit for. To put it another way, don’t just participate in a problem because you want to. For example, your area of interest is text analytics using Natural Language Processing. It might be a skewed effort if you participate in a credit card default problem focusing on predictive analytics.

3) Read motivational books:

According to some Data Science competition champions, it is very useful to stay motivated by reading inspirational books during the time of participation.

4) Sleeping over the problem:

Our mind has a unique power to work on some problems during our sleep. Some champions utilize this subconscious capability to their advantage.

5) Understand the problem:

This is an important point we all will agree upon. To explain, we won’t be able to reach the best possible solution or start in the right direction towards a solution if we do not have a proper understanding of the problem.

6) Using discussion forums:

A participant does face issues and challenges while solving a problem. Not only can one post problems and find solutions in these discussion forums but also find many useful tips about the solution here.

7) New packages:

The world of Data Science is a very dynamic one with regular updates and new developments. Trying new packages to solve the problem can enhance your learning experience and keep you up to date with the latest.

8) Start with simple models:

No matter how tempting applying complex models might seem, it is always good to start with simple models. This helps you learn and realize the importance of simple models. Also, many times you do not even need to move to difficult models on some problems.

9) Try top-performing models:

Some algorithms are commonly used in competitions and generate better results. For example, XGBoost is pretty common and a top-performing model lately.

10) Garbage in Garbage out:

Data available needs to be cleaned, trimmed and brought in line with the problem we are required to solve. In other words, feeding a lot of unnecessary data in any model, in turn, affects the accuracy of our model.

Conclusion:

We encourage every student to participate in at least one contest of his or her liking. This will ensure enriched learning apart from the curriculum. Do let us know if you have any doubts related to Data Science contests. Additionally, our Project Assistance team is just one click away from helping you in completing the competition you participate in.

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