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Monday, August 21, 2023

How Data Science Project Works - From the Koobiyo Teledrama

About five years ago, the Koobiyo teledrama was very popular due to the uncharacteristic nature of the teledrama. It was a political teledrama, one reason the teledrama became popular. However, this post is not to discuss the political side of the tele drama but to discuss the data science side of it. 



Jehan, the main character of this teledrama, has the idea of building a tool to predict the future problems of people. He uses decision tree architecture to achieve this and explains his idea to his companion, Priyantha as shown in the below video. Watch from the 540th second.


So his idea is very clear. By using data like Gender, Marital Status, Professional Qualification, Parents' details, Friends' details, etc. 
After building the predictive software, he presents his case to his good friend Hiruni and his developer too joins the discussion in the following video. Watch from the 40th second.



After observing the work done by Jehan, Hiruni was impressed, but she had a very vital question to ask. "Why do you think people will buy this". Jehan was a little perplexed by the question, and he stressed that this is the software that everyone will have at their fingertips. Obviously, people know their problems they need solutions, not to suffer from their own problems. 
This is something important to us as data scientists. We are very good at technical details, but we need to understand the solution part of the tool. Remember, people need a solution to their problem not a technical tool. 
Then Hiruni provide a solution to Jehan and asked him to provide this to a human resource person to predict the future problems of its own employees. 
Then the solution can be viewed in the 21st second of the following video.



In the live demo, Jehan proves that it is important to select the best people for the organization from this software. It is clear how we should think from the business point of view rather than focusing on technical aspect mainly. As a data scientist, we need to look at the solution side of it.

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