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.
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
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)
You are right, nice post!