For years, since the emergence of big data and analytics as a profitable career path, a heated debate has been going on within the community regarding the difference between a Data Scientist vs Data Engineer.
For folks, who are looking to enter this career path or build a big data team, it is important to be able to tell the difference between the two seeming familiar professions for belonging to Artificial Intelligence and Machine Learning vertical and ultimately making a knowledgeable decision to go down which career path.
New job positions like ‘Data Engineer’ were created as the Data Space matured a bit and demanded unique skills to handle big data initiatives. Data Engineers and Data Scientists, each have their own set of duties, skills, and responsibilities.
In the Data Science team are the Data Scientists. They are in charge of organizing the data provided by the Data Engineering team. They, then, use this data to build sophisticated models by applying statistical, predictive, and prescriptive modelling. Their job also, sometimes, involves analyzing the data for hidden patterns in them.
A data engineer is responsible for developing, testing, and maintaining structures like databases and large-scale processing systems. They have to deal with raw, unprocessed data. This data, sometimes, might not be validated and may contain suspicious records.
It is the job of the data engineer to improve data efficiency and quality. They may do so by using a number of programming languages and tools. They are also in charge of delivering data to the Data Science team.
Among the Data Scientists, without a doubt, the most popular tools are Python and R. When using Python and R for Data Science, Data Scientists often resort to their packages like ggplot2, which is used to make appealing visualizations in R and then, Pandas in Python, which is used for data manipulation.
When working on data science-related projects, there are several other tools utilized like NumPy and Statsmodels.
Data Engineers, on the other hand, heavily rely on tools such as Cassandra, MySQL, SAP, MongoDB, Oracle, Hive, etc. The main distinction between Data Scientist vs Data Engineer is that while a Data Engineer is supposed to visualize the data, a Data Scientist is supposed to interpret the data. And this is reflected by the tools that each profession utilizes.
Well, if there is one thing that everyone agrees on, then it is that Data Scientist vs Data Engineer, they both come from computer science backgrounds.
It is not uncommon to have Data Scientists come from educational backgrounds like operations research, statistics, econometrics, economics, etc. Data Engineers often have Engineering backgrounds, mostly in computer science engineering.
Data Scientists have more business acumen than Data Engineers. However, that in no way means that you will not find data engineers, who come from operations and business acumen backgrounds.
The whole data science industry is made up of professionals who come from diverse educational backgrounds. It is not nearly as strange as it should be to find physicists and biologists, sometimes, even meteorologists find their way to data science.
The demand for people, who have a genuine passion for data science, will always be there. The job outlook for these folks is highly positive.
And as for data engineers, the number of job postings online to hire them has gradually increased over the years as well.
Even in a medium market for Data Scientists, their paycheck is set at $135,000 annually. And the lowest that a Data Scientist can earn is about $40,000 per annum while the maximum limit is set at over $300,000 per annum.
Compared to Data Scientists, the paycheck for Data Engineers is set a bit lower. In a medium market, a Data Engineer can earn $124,000 yearly on average. The lowest that their paycheck can go is at about $30,000 per annum while the maximum that their paycheck can go up to is over $300,000 per annum, the same as a Data Scientist.
As we conclude our blog ‘Data Scientist vs Data Engineer – What’s the difference and which profession is better?’, we have arrived at the conclusion that both professions have their own sets of merits and demerits. While the paycheck of a Data Scientist may be higher than that of a Data Engineer, it will be interesting to note that companies, these days, are looking for well-composed data science teams rather than only one data scientist, possessing all the skills and it is difficult finding people who embody those qualities that fit the company’s profile.
One could argue that the ‘Data Scientist’ bubble is bursting, just like the tech bubble of the nineties, or it is eventually going to burst in the near future.