Data Scientist and Data Engineer in the ideal world!

Though it is too early to differentiate between the two roles and responsibilities but still it is nice to have a little understanding of them. Most importantly, both of these roles are important in a well data science world!

Some how this is a common thing which many people get confused with. So in the ideal world Data Scientists are generally people who understand various statistical model and can find out how a problem can be solved using the data around. On the other hand, Data Engineers are the people who implement the ideas of the Data Scientist to create the technical architecture which would be a technical implementation of the solutions.

So now it would be clear that, skills required for Data Scientists are strong Mathematical knowledge and very good understanding of Statistical modeling with problem solving capabilities. Additionally a little skills of programming is also required to become an eligible member for this position.

On the contrary, skills expected from a Data Engineer would be a strong technical knowledge and programming skills and ability to formulate technical solutions. A little statistical knowledge would come in handy. Although in the real world there is a lot of overlap between the two roles. But what is to be understood is that, you do not grow from a Data Engineer to a Data Scientist or Data Scientist are more important. Data Scientist and Data Engineers have different roles and responsibilities and skill sets. So learning hadoop or any other similar tools and technologies doesn’t mean that you will be a Data Scientist but having a good exposure to other mathematical skills and knowledge would be a bigger strength in order to become a Data Scientist.

101.datascience.community presented the difference by using an excellent Venn Diagram.
data-scientist-vs-data-engineer

Difference

So when choosing career or hiring someone for this roles, please choose wisely and understand that they are different roles and responsibilities.

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