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

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

What data science skills are really required to excel on the job? Why are competitions important but only partly valuable? And how to 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 both in competitions as well as 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 to this 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 in order to derive valuable insights and build a solution; a focus only on modeling won’t bring you far

Anastasis Stamatis addresses a very important point, which is that learning data science skills can be quite 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 required next to technical aspects

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

  • 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



About Omdena

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5 Reasons Why AI Hackathons Won’t Build Real-World Solutions

5 Reasons Why AI Hackathons Won’t Build Real-World Solutions

To create meaningful innovation and build real solutions, we need to move beyond (AI) hackathons.


By Omdena Founder Rudradeb Mitra 



Hackathons are perceived as a fast track to innovation. Creative minds come together and solve problems. This all sounds good in theory but let us look at the facts.

For example, there have been dozens of hackathons in response to COVID-19, 1000s of people are giving their time to build solutions. I even mentored one of the largest, where over 1000 engineers participated. The organizers put days, if not weeks, of work into it. So I truly commend their efforts and their goodwill. However, without taking away anything from them I question the effectiveness of hackathons.


Why AI hackathons won’t build real-world solutions


Reason 1: Lack of domain expertise

Social problems like COVID-19 cannot be solved only by engineers. To build real-world solutions we need to involve policymakers, domain experts, users — something that teams participating in hackathons often don’t have access to.


Reason 2: Absence of diverse backgrounds leads to bias

Hackathons are often formed by teams who know each other and thus lack fresh ideas. We have seen that systems developed only by engineers end up being biased. A “diversity disaster” has resulted in flawed systems that amplify gender and racial biases according to a survey, published by the AI Now Institute, encompassing more than 150 studies and reports.

The report summary says an overwhelmingly white and male field has reached ‘a moment of reckoning’ over discriminatory systems

In addition, when I was mentoring hackathons, I can say that out of 100+ ideas — we can summarize all of them in 10–15 ideas. Most of them were similar due to similar backgrounds of the participants.


Reason 3: Too little development time

Most hackathons run only for a few days or weeks, which is not enough to build or test solutions replicable in production environments.

In a hackathon often you are motivated to impress judges but in a real-world project, you are building entire solutions. Building a prototype is easy, but there is customer research, product marketing, design, and sales, which will determine if the prototype is actually implemented and creates value.


Reason 4: Competition vs. Collaboration

Why do we need hundreds of teams to compete solving the same problems while we could have several teams collaborate so solve multiple problems?

In one of my previous articles, I argue that competition-based models like Kaggle are not the best approach to build real-world solutions. Among many reasons, a key issue is that people are incentivized to win instead of working together to find the best solution to a problem.

For example, in a recent competition, a Kaggle 1st place winner cheated using a fake dataset to get $10,000 prize money and gain exposure.


The alternative? A collaborative approach

To solve the aforementioned problems and create an environment for building real-world solutions to problems like COVID-19, we founded Omdena.

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

Omdena’s Covid19 initiative displayed at Times Square NYC

Omdena’s Covid19 initiative displayed at Times Square NYC


Contrary to AI hackathons we embed the following mechanisms to build inclusive and deployable solutions.


1. Fostering global collaboration

Most of today’s challenges are global in nature. It is not that one country or group of people can solve it.

To find solutions we need a model where global communities can come together to solve problems, share their data and build solutions. In the world of Big data, AI and Machine Learning, data is key. It is not a sophisticated algorithm or a better team, but it’s the team with better (and more) data that wins.


2. Following a bottom-up model to enable creativity

I firmly believe the future of innovation is bottom-up, where communities come together to collaborate and solve their problems. Communities will drive the future of AI, not Governments or Corporations. I argue that communities have both intrinsic and extrinsic motivation to solve the problem, which is essential in building solutions.

And the evidence is clear. At Omdena our global community in more than 75 countries develops innovative solutions every month. Up to 50 engineers and domain experts collaborate for two months where ideas are shared openly to find the best-fit solution.

With a bottom up approach, those who are more involved with the specifics of their field are included in the ideation and brainstorming process, with the result being a more harmonized and inclusive development system.


3. Working closely with domain experts

Most real-world problems are not limited to just a data science problem but require domain experts to create value. In our projects, domain experts work in a constant exchange with the AI teams to help the company refine the problem, prepare the data, and build a solution that is applicable in their context.


4. Involving people who face the problem

Something which goes beyond domain expertise is to incorporate people who faced the problem. This brings empathy and can help to reduce bias.

On Omdena’s innovation platform almost 1000 AI engineers from 79 countries have collaborated to build real-world solutions within a time frame of two months. The benefits of Collaborative AI are enabled by the people who are part of the project. We believe rigid top-down management principles or winner-takes-all approaches create the wrong incentives for a world where solidarity and teamwork are more important than ever.


Human first, technology second

In conclusion, I am arguing:

All of us interested to build an inclusive future, need to think more holistically to create an environment beyond gender, race, and cultural backgrounds and focus on how we can collaborate as humans.

Digitization and AI have enormous potential for doing good in all aspects of life and in all sectors of the economy. However, it is the combination of people with technology that truly enables progress and higher productivity. We have to emphasize community and purpose. That is the key to create meaningful innovation and products.


About Omdena

Building AI Solutions Collaboratively 

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

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