Difference between Data Science and Data Engineering : Responsibilities, Tools, and Skill

Introduction:

The 21st Century is the century of Data and Data is flowing everywhere which is increasing exponentially. Data Engineering and Data Science are the most buzzing word around data nowadays. But they are not the same, they have different meanings, roles, and functions but they complement each other. In this article, we will try to differentiate both Data Science and Data Engineering by describing their roles and responsibilities.

Data Science is the multidisciplinary field combining Computer Science, Mathematics, Statistics and has a strong Business Domain Knowledge while Data Engineering is the subset of Data Science. Data Engineering focuses on designing and building Data Science pipelines that can collect, prepare and transform both structured and unstructured data for the use of Data Scientists. After that Data Scientist comes into action extracting meaningful patterns and insights from the datasets using the tools, methods, and algorithms.

That is why Data Engineering complements Data Science in other words Data Engineering is a support system of Data Science.

Responsibilities:

Data engineers collect the data, move and transform the data into the pipeline for the data science team for that they write queries on data. They develop a data warehouse with the help of ETL(Extra Transform Load) while Data Scientists analyze, test make conclusions from that and make model make predictions using the given dataset.

Tools

Data Engineering works with advanced programming languages like JAVA, Scala, Python, and Big Data frameworks like Hadoop, Spark, etc. while Data Science works on Python and JAVA and advanced analytical tools like Tableau.

Skills:

Both Data Engineering and Data Science require a quite different skill set to work. Like Data Engineer work mainly on developing data science pipeline while Data Science requires Analytical knowledge.

Salary:

As per the Ambition Box :

• The Average Annual Salary of a Data Engineer in India is  ₹8.3 LPA 
• The Average Annual Salary of Data Scientists is ₹10.5 LPA.

 

Conclusion:

Data Engineering allows Data scientists to focus only on performing analysis, to save their effort and time i.e. Data Engineering makes Data scientists more productive. So both are important parts while concluding any given data set. They are not entirely different from each other they are complements of one another. Data Engineer works on the raw data and they are responsible for the accuracy of the data while Data Scientists works on the manipulated data given by Data Engineers and they create a connection between Customer and Stakeholders.

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