Data Science: Myths Vs facts

Not everyone knows that data scientists invest 80% of their time in cleaning and transforming data and 20% of their time is dedicated to the data modeling part, so a data scientist who wants to create very accurate data as well as a machine learning model, he needs to clean and transformed data. We know that when we work on a particular Big Data solution, there are multiple steps involved in it and the first and very important part is transforming the data. Nowadays, we receive data from multiple sources, and the raw data sometimes contains errors, as well as junk records. If we cannot clean our data, we will not be able to obtain meaningful transformation data, and we will not be able to create machine learning models that are very accurate. That’s why data science is not just about building the predictive model and regression models, it’s a good mix of cleaning and transforming the data, and then building accurate machine learning models.

 

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