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What Makes Us A Failed Data Scientist !

Data science was once termed sexiest job of 20th century. With the development of user friendly modules like scikit-learn, tensorflow etc. implementing a machine learning project became a kids job.

With all these hype and rush to learn and be a data scientist ,we have to remember these basic protocols so that we don't end up being unsuccessful despite our hard work.

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So , these steps will definitely make you a FAILED DATA SCIENTIST which one has to refrain from when starting with DS :

● learning data science libraries before learning coding basics

● learning ML algorithms before learning how to preprocess your data

● learning deep learning before machine learning.

● learning data visualisation before understanding the basics of statistical inference

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So , what can be done right ?

● You have to know coding basics before you can even debug the implementations of your DS/ML libraries

● You have to know how to preprocess your data before applying machine learning methods accurately.

● You have to know statistical inference before you make sense of your visualisation.

Conclusion :
Remember to do things in a right way. Because , we may have to begin all over again in case we get our first steps wrong.

Thanks For Reading!

Top comments (1)

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Maximiliano Schvindt

You are right, nice post!