The Difference Between Data Scientist And Data Engineer

Data science courses are selling like hot cakes in institutes and online education platforms. But, like every other course, data science too has many offshoot roles such as data analyst, data scientist and data engineer. Whether an individual is planning to learn about data science or building a team to work on data, they should be aware of these nuances. As the field evolved, several new roles also came up to meet certain functional demands. Two of the most misunderstood roles in data science is perhaps that of a data scientist and data engineer.

As with any other overlapping roles, data scientist and data engineer too have several similarities, but both roles need certain expertise to enhance business performance. Let’s understand these roles, starting with a data engineer.

The role of a data engineer

The infrastructure for the data to be analyzed is prepared by a data engineer. The responsibility of a data engineer includes data formats, data scaling and data security. Data engineers may have an engineering degree, but they definitely have a solid programming knowledge. Languages such as Python, R, Julia, Matlab, etc. are the pre-requisites of becoming a data engineer. A strong knowledge in networking is also needed for data engineers to create and manage distributed computing systems to distribute and analysis the tremendous amount of data. A data engineer will gain preference with knowledge in mathematics, probability and statistics.

The role of a data scientist

 Data science and machine learning are two methods used to make sense of the large volumes of data that is available for businesses from which they get valuable insights, thus helping them take informed business decisions. The first role in data science belongs to data scientists. Data engineers came later on. Before the engineers, it was the data scientists who managed the infrastructure as well. But, after data engineers took over the role, data scientists became focused on reading and analyzing the data and extracting meaningful insights from it.

But, do not be confused. Even though, there are some overlapping factors, both are standalone skills that complement each other to achieve a common business goal. Overlooking the common skills required, it can be said that data engineers are good at programming and data scientists are good at data analysis. The difference is that data scientists need to learn some coding to automate few process due to the large volume of data. Data engineers, however, need not learn about data analysis as their role is more focused on setting the infrastructure for specific tasks or goals.

The infrastructure developed by the data engineers are used to generate data from various sources. The role of a data engineer is limited to data; they are not involved in other computational requirements of the organization.

Data scientists get involved when the data engineers provide them the data to be analyzed. They, the scientists, work on the data using their aptitude in statistics and probability to identify trends and patterns, and get insights for business opportunities. The infrastructure built by the engineers is utilized by the scientists to conduct research. Data scientists also interact with the senior management to provide them with meaningful customer insights and get feedback on the needs of the business.

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