To Learn Real-World Data Science Joining Competitions Will Not Be Enough
How to acquire the full set of skills you need as a data scientist.
This article has been co-authored with Erum Afzal.
“Basically, you, me, everyone, we want to learn and leap and then repeat.” — Whitney Johnson
Data Science, Machine Learning and Artificial Intelligence is meant to stay and are being used to solve problems in almost all disciplines of life.
You might already know that there are plenty of online resources that allow you to acquire relevant knowledge for a data science career.
The theoretical basis of data science skills
You need skills in statistics, development, and business/domain knowledge. For statistics and development, there are various resources that take us from a basic level to a theoretically advanced level.
However, the question is, how can I take the theory learned and apply it to a real-world problem? How can I transition from the academic world to real-life?
The best way to do this is by getting your hands dirty by working on real-world problems by joining hackathons or online competitions.
The role of AI competitions
Competitions are increasingly popular and are playing an important role for data science enthusiasts who want to test and improve their knowledge in a real problem and also be able to compare the results of their models with others.
Occupying the best positions gives you the satisfaction of winning a prize and additionally, you can gain recognition in the competition platform.
“Whether it’s Google or Apple or free software, we’ve got some fantastic competitors and it keeps us on our toes.” — Bill Gates
While if you join a competition you’ll improve your technical skills by implementing models, you fall short on the following.
Why competitions are not enough
In a competition, the data has already been extracted from an information system, while in the real world there are a various skills needed to get the data, prepare it, refine the problem, and coordinate across teams.
Lack of communication
In a real-world setting, different working groups are set up to solve the problem which consists of various roles and each one has the function of supporting the solution of a problem. Without proper communication skills, no solution could be built.
Refining the problem
A data scientist must have more skills than just implement a model, sometimes companies have data but do not know how to translate a problem into an AI context, or the problem they are posing cannot be solved with their data.
There are plenty of scenarios that you can find in the real world where a data scientist needs to show mental flexibility.
Team work
Complex problems require diversity of thought and participating in competitions means to compete against rather than work with each other. If you want to make an impact in your career, you better learn to work in a team.
The importance of collaborative solutions
In collaborative environments, the group works towards obtaining a successful team result.
Knowledge is shared and the best ways to address the problem are discussed.
You, as a data science enthusiast, improve your skills since in these environments people with basic, medium and expert knowledge help each other.
The role of Omdena in addressing those gaps
Omdena offers collaborative environments, where for eight weeks around forty people work together to find a solution to a problem defined:
These problems have a social impact and by solving them you improve the lives of people on the planet.
Collaborators work as an empowered team and split the problem into subtasks, then each person defines in which task they want to collaborate.
Everyone in the team has the passion to learn and collaborate to build a solution that causes a positive impact.
Omdena’s challenges range from fighting hunger, preventing sexual harassment, detecting wildfires, to analyzing tweets for preventing violent acts.
Conclusions
- Collaborative environments allow you to meet people around the world with your same interests, so you build a network of colleagues or friends where you could find help in the future.
- To working in real-world problems, and real platforms of infrastructure help you to build technical skills that you could require for a new job.
- In a collaborative environment, each member has a role defined, you make periodical meetings by task and by the project to monitor the project and objectives achieved. It helps you to improve your communication skills.
Want to become an Omdena Collaborator and join one of our tough AI for Good challenges, apply here.
