Differentiating Factors between Data Scientists, Data Engineers, and Data Analysts

When we talk about our data scientists, data engineers, and data analysts, we might think that the nature of their jobs is the same one way or the other. We think of it that way because all of them are eventually dealing with data. But in reality, their tasks are the ones that become a differentiating factor between these three. Though these three might be working under the same roof and communicating with one another.

Yet their tasks, roles, duties, etc are totally different from one another. At some point, they might join forces but in general, their tasks are different. So let us take a look at what factors really differentiate them from one another.

 

Differentiating Factors between Data Scientists, Data Engineers, and Data Analysts:

Data Scientists:

Data scientists are the ones who use scientific methods, processes, and algorithms to extract knowledge from both structured and unstructured data. Precisely, their task or main role is to unearth future insights from completely raw data. All the data scientists use dynamic techniques such as Machine Learning. With the help of these techniques, they are able to gain future insights.

Similarly, the responsibility of data scientists is also to take an active part in the decision-making process. This responsibility is quite serious and a genuine one as whatever the decision the data scientists make is going to affect the course of the company. So they have to be very confident over whatever the decision they make regarding the company. Plus they have to not just deal with structured data but unstructured as well.

Data Engineers:

Data engineers are mostly known as software engineers by trade. Their task is to compile and install database systems. Similarly, other tasks involve writing complex queries, putting disaster recovery systems into place. Furthermore, they are also responsible for writing a variety of complex queries.

The data engineers are in fact the ones who are responsible for creating such a platform for both data analysts and data engineers where they can work on. But it should be kept in mind that though they develop a platform for data scientists and analysts to work on, yet they don’t have a say in the decision-making process. This means that they are only responsible for developing and maintaining data pipelines. Besides this work, they don’t have a say in other tasks.

Another factor that differentiates data engineers from the other two is that they don’t have to be proficient in data visualization. But unlike the data analyst, the data engineers have to deal with both structured and unstructured data.

Data Analysts:

The data analysts are the ones who have to analyze, cleanse, transform, and model the data in such a manner to extract useful information from it. The data analysts mostly take actions that are always affecting the company’s current scope in a good way. Like data engineers, data analysts also don’t participate in the decision-making process. But what they offer here are the static insights about the performance of the company which can help in decision-making.

Similarly, the data analysts use static modeling techniques which help them to summarize the data through descriptive analysis. Another factor that can rule out data analysts from the other two is that all the data analysts have to deal with structured data only.

          

Conclusion:

We think that the above information about the data scientists, engineers, and analysts is enough for anyone to understand the differences between them all are used in Data visualization. Tableau is most popular software for data visualization . Tableau is learn by tableau certified persons if you want to become tableau certified you have to take tableau certification exam or tableau certification dumps to pass the real tableau exam. So if you are that person who has ambiguities regarding the role of these three, then you might need to read this whole topic with full concentration to understand.

So enjoy reading this topic, stay home, stay safe, and never stop learning.