Hackathons–to filter outliers

Hackathons–to filter outliers

Now a days, whenever I go through Google suggested articles in my browser there is at least one topic about online Machine Learning Hackathons going on and most of the Hackathon organizers are giving job/interview opportunity as a prize to the winners.

Have you ever wondered why companies are arranging Hackathons? Hackathon is used as a tool to hire top talents. Most companies no longer rely on long interviewing regimes or questions like ‘Why do you think we should hire you?’. Traditional hiring parameters like interviews, grades, or past experience might miss out outliers who lack real coding abilities.

In simple words, ‘Data Mining’ is defined as a process used to extract usable data from a larger set of data available.

One of the Data Mining technique is ‘Outlier Detection’ aka ‘Anamoly Detection’. This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. Determining the data that is important and reliable can assist data scientists in making better predictions in a wide range of industries. It is therefore very important to detect and adequately deal with outliers.

In our Hackathon (talent mining) analogy, only top talents (usable data) are interviewed/hired from vast variety of data set(talent pool) available after filtering(Outlier Detection) people who are outliers in terms of knowledge and experience.

By: Rameshwari Naik

 

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