Are Competitions Enough to Learn Data Science Skills to Excel on The Job?

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May 21, 2022
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Are Competitions Enough to Learn Data Science Skills to Excel on The Job?

What are data science skills required to excel on the job? Why are competitions important but only partly valuable? And how do I acquire a more holistic skill-set through collaborative projects? Those are only some of the questions we answered in this live webinar.

Learning from the experts

We discussed how to leverage competitions, hackathons, and collaborative projects to become a better Data Scientist.

Who could be better suited to answer this question than six real-world data scientists from diverse backgrounds who previously participated in competitions and collaborative Omdena projects?

In the webinar, AI researcher Erick Galinkin points out that “competitions are valuable to improve specific problem solving and modeling skills while you can compare your results with others for a benchmark analysis.”

Murli Sivashanmugam adds that “In a competition, the data is fixed, but in the real world, the data evolves, and one needs to be able to work with both changing data and models.”

In the words of Dawid Mondrzejewski (15 years in Business Analytics) and Data Scientist Julia Wabant, “one of the most important benefits of collaborative projects is that you need to explain concepts, which deepens your understanding while helping others to grow.”

AI community leader from Kenya Kennedy Kamande Wangari points out that “both in competitions and collaborative projects, you need to understand the business context and problem to derive valuable insights and build a solution; a focus only on modeling won’t bring you far.

Anastasis Stamatis addresses a critical point, which is that learning data science skills can be pretty challenging and no matter if you are in a competition or collaborative project, “being in a supportive and safe team where you can grow through open dialogue and asking questions is essential to move forward in your career.”

Data Science Skills Required

Erick Galinkin, Julia Wabant, Anastasis Stamatis, Murli Sivashanmugam, Kennedy Kamande Wangari, Dawid Mondrzejewski

Essential data science skills are required next to technical aspects

As a result of the lively discussion, all panelists agreed on the following skills as crucial:

  • Collaborative work
  • Problem-solving skills
  • Business/domain knowledge
  • Data Engineering / Working with a messy data set
  • Communication & Storytelling
To learn about the entire data science skills checklist, you can watch the webinar discussion below:
  • Min 1:30: The value of competitions and hackathons and how to use them effectively 
  • Min 20:00: Where collaborative projects differ compared to competitions 
  • Min 35:20: How collaboration results in job-relevant real-world skills 
  • Min 61:10: Tips & strategies to use the time to prepare for a Post-COVID career

 

 

Tagged: Career Tips

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