Students often get confused between data science, data analytics, and data engineering while applying for higher education. This article gives you a fundamental understanding of what they are in a nutshell.
Data science is an amalgamation of expertise on these four basic pillars: business or domain knowledge, statistics or probability, computer science, and written, verbal communication.
Data Scientists are programmers with advanced knowledge of statistical interpretation, probability, and machine learning procedures. They generate new questions from identifying various patterns of the large data to give inputs for the business. Depending on various patterns, they see how the data can be useful for the business. They use much more advanced modeling techniques compared to the other two. The analysis for business intelligence is done using R and Python.
Data analytics is the use of data mining techniques and is of a more visual platform, they receive instructions from top-down to achieve goals, perform analysis and submit reports, unlike data scientists who create their own goals with advanced techniques. Data Analysis is a much more visual platform when compared to data science. Analysis for business intelligence is done using Excel (for visualization and pivot tables), Tableau, SAP, and Qlik.
Data Engineers deal with more of the non-functionality aspect of big data. The data that is received will be from different sources in different formats. They must be extracted, moved, converted, assimilated, and stored into one particular format that is optimized for analysis and business intelligence. They are concerned with the data architecture, computing, data storage structure, and data flow.
The fundamental difference is that data analysts and data engineers are typically neither programmers nor statistical modelers. The tools they use are very different from the tools and techniques used by data scientists.