Why Data Science Competitions are Important & How to Get Started by@davisdavid

Why Data Science Competitions are Important & How to Get Started

A data science competition is a series of data science challenges to solve complex business problems and share top solutions to the organizations that own the data. Participating in competitions has been one of the best approaches to help beginners in data science get more experience and finally apply for job opportunities. Kaggle, Zindi Africa and Data Hack by Analytics Vidhya are the most popular data science competitions platforms around the world. There are more than 20 competitions platforms that offer different types of competitions you can participate in.
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Davis David

Data Scientist | AI Practitioner | Software Developer. Giving talks, teaching, writing.

To become a Data Scientist, you have to learn, gain the required skills and practice a lot to get more experience. Participating in data science competitions has been one of the best approaches to help beginners in data science get more experience and finally apply for job opportunities. 

In this article, I will talk more about data science competitions and how you can get started today.

What is a Data Science Competition?

A data science competition is a series of data science challenges to solve complex business problems and share top solutions to the organizations that own the data.

Organizations can find the best solutions to their complex business problems that will help them improve their business while enabling data scientists to learn from the experience and win awards/prizes.

5 Data Science Competition Platforms

There are more than 20 data science competitions platforms around the world, but in this article, I will mention a few of them that are popular and have a lot of different types of competitions you can participate in.

1. Kaggle - is the world's largest online community of data scientists and machine learning practitioners. It offers different levels of competition and allows its users to publish datasets and build models in the online environment.

2. Zindi Africa - is the largest community of data scientists in Africa, working to solve the most pressing challenges using machine learning and AI. Zindi Africa helps its users to learn, hone their skills, and find job opportunities.

3. Data Hack by Analytics Vidhya - the platform offers data science hackathons and competitions on different levels. Users can compete, win, learn and build their data science portfolio. 

4.DrivenData - it provides different data science competitions to build a better world. Most of its challenges focus on social impacts in the area like health, education, research, public services, and conservation.

5. Machine Hack - is the online platform that hosts machine learning competitions to solve the toughest business problems from different companies. It also helps companies to find the best talents in data science for job opportunities.

Recommended Data Science Competition Platforms for Beginners

For beginners in data science and machine learning who want to put their skills into practice, I recommend you select one of these data science platforms (kaggle, Zindi Africa, or Data Hack by Analytics Vidhya). These platforms offer a lot of resources and guidelines to help you start participating in data science competitions. 


Benefits of Data Science Competitions

Without a doubt participating in data science competitions has a lot of advantages for both parties, organizations that have a complex business challenge, and you as a data scientist. Here are a few of them: 

  • Test your skills against top talent.
  • Learn by doing and gain exposure.
  • Earn income by doing what you enjoy and do best.
  • Build your profile and attract potential employers.
  • Apply for data science job opportunities.
  • Learn from others through collaboration and discussion.
  • Networking with like-minded people.

Types of Data Science Competitions

Most of the data science competitions are grouped into three categories.

  1. Prize Competitions - These competitions provide monetary rewards to the top winners at the end of the competition. Most of the time, the top three winners on the leaderboard are the ones with paid rewards.
  2. Points or Medal Competitions - Points and medals are used to recognize your participation in the data science challenges.
  3. Knowledge Competitions - These competitions are recommended for beginners since they provide guidelines, learning materials, and how to participate in the competition and submit your submissions.

How to Get Started

If this is your first time and you want to start participating in different data science competitions, I have prepared a few steps that you can follow to achieve your goal.

Step 1: Choose a Platform 

The first step is to choose one data science competition platform to join and start participating in different competitions.

As I have said, I recommend you choose one of the following data science competition platforms.

  • Kaggle
  • Zindi Africa
  • Data Hack by Analytics Vidhya

Step 2: Register and Create your Profile 

This is a very important step, especially in the process of creating your profile. Make sure you fill in all information required and add your best profile image. As I have said, potential employers use these data science competitions platforms to find good talents in data science. If your profile is incomplete, you may lose a good job opportunity.

Step 3: Select Competition Based on your Level


Most of the competitions are in different levels, such as beginner, intermediate and advanced levels. If you are a beginner in data science, I recommend you choose beginner competitions.

Step 4: Read and Understand the Details of the Competition 

It is very important to read most of the details about the competition. Most of the competition contains the following details that you must read and understand before you begin to participate 

  1. Problem statement
  2. Objective of the competition - what you are going to solve
  3. Datasets for the competition - train set, test set, and other important documents 
  4. Evaluation metric - how your solution will be evaluated.
  5. Rules - what are the dos and don'ts of the competition.
  6. Timeline - when the competition starts and ends.

Step 5: Create a Team and Work Together

I believe in teamwork, and if you are a beginner, you will learn a lot if you will participate in a competition with your fellow teammates. This will be a good opportunity to share different ideas and approaches to solve the same problem and see which one can produce a good performance.

"If you want to go fast, go alone. If you want to go far, go together."

On September 18, 2009, BellKor Pragmatic Chaos officially won the NetFlix competition by a tiebreaker.

Note: I recommend a team to have at least 2 or 3 members.

Step 6: Ask Questions on the Discussion Page

Most of these data science competition platforms provide a discussion space for you to ask any question you have concerning the competition you are participating in. So don't hesitate to ask any questions because asking questions help you learn more from others.


You can also respond to other people's questions if you know the answer; this will boost your confidence when sharing what you already know, even if you are a beginner in data science. This is a win-win situation. 

Step 7: Start With One Competition

If you want to learn and build your data science skills through participating in competitions, don't jump from one competition to another one without finishing the previous competition you have joined.


Take your time to participate in one competition and make sure you finish it before moving on to another competition. This is very important because, at the end of the particular competition, you need to take some time to evaluate yourself on what new skills you have gained and where you need to improve.

Step 8: Read and Understand Other Participant's Solutions

When the competition is closed, top participants in the leaderboard tend to share their solutions(codes) on the discussion page or in their Github repositories.

This is a good opportunity for you and your teammates to learn from other participants' solutions and the best approach or technique to solve the same challenge. 


You can download the source code and run it on your local machine, and then you can apply what you have learned in the next data science competition you are planning to join.

What Next?

In my next article, I will write more about the techniques to help you get higher performance in data science competitions.

If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post!

You can also find me on Twitter @Davis_McDavid.

And you can read more articles like this here.

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