Difference between Data Scientist and Data Engineer

Data Engineer: Data engineers are the ones that prepare the data, which is further analyzed by the data scientists or data analyst. Data Engineers go into extracting, collecting and integrating data from various resources and manage that data. A data engineer does not depend upon anyone. Also, a data engineer just collects data thus his suggestions in the decision-making process of a company are not needed.

Data Scientist: A Data Scientist works on the data provided by the data engineer. A data scientist analyses the data and gives insight as to how the company should work based on that data analysis. A data scientist is dependent on a data engineer. When it comes to decision-making the analysis of data scientists is considered.

Data-Scientist-vs-Data-Engineer

Below is a table of differences between Data Engineer and Data Scientist:

S.No Data Engineer Data Scientist
1 “Architect” of the data “Builder” of the “architect’s” plan
2 Extracts, Collects and Integrates data Analyses the data provided by the engineer
3 Independent Dependent on the engineer’s data
4 No say in the decision-making Analysis of data scientist is considered for the decision-making process of a company
5 Data Warehousing, ETL, Advance programming, Hadoop, SQL, Data architecture and pipelining, Machine Learning, etc. are the skills required R or Python or SAS, statistical analysis, Apache Spark, Machine Learning and AI, Data Visualization and data mining are the skills required.
6 Is responsible for the accuracy of data. Creates a connection between a stakeholder and a customer.
7 Deals with raw data Deals with the data manipulated by the data engineers
8 No need to have any storytelling skills to convey the result Needs to have storytelling skills to present the analysis

Although the two are different from each other but are essential parts of an organization’s body. Both are incomplete without each other and are complementary to one another.

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