Career Guidance - Data Scientist versus Data Engineer: How are they different?
Data exists everywhere. In the current era of technology, digital data is expanding exponentially in the digital universe. Lately, much has been written about the different areas of data science roles, particularly the difference between data engineers and data scientists. The upsurge is specifically due to the fact that the new business models have replaced old ones. Qualitative data has superimposed quantitative data and the results are pretty mind-boggling.
The role of a data engineer has moderately gained momentum along with a data scientist.
Data Scientists' Responsibilities
Generally speaking, data scientists arrange for cleaning, maneuvering and organizing big data. They use statistical measures and machine learning programs to assemble data used in prognostic modeling. They conduct industry research and rigorous data analysis to answer business requirements effectively.
Data Engineers' Responsibilities
The data engineer is a person who fosters, builds, investigates and supports architectural databases and maintains enormous processing systems. Data engineers handle raw data that might consist of human, machine or instrument errors. They should recommend solutions and implement ways to improve data dependability, efficiency, and quality.
Though these two profiles seem to overlap significantly there are a lot of differences, especially in the following areas.
A. Educational Qualifications
B. Tools, Languages and Software
C. Pay Structure
D. Job Outlook and Perspective
Do you want to read more about each area and see what is the difference? Then go » here! «