Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage.
The growth of data and its usage across the industry is hidden from none. During the last decade in general, and the last couple of years in particular, we have seen a major distinction in the roles tasked with crafting and managing data.
Data Science is without a doubt a really growing field. Organizations and even countries from across the globe have experienced a drastic rise in their data collection endeavours. With numerous complications associated with collecting and managing data, this field is now host to a wide array of jobs and designations. We now have data scientists who are grouped into more specific tasks of data engineers, data statisticians, and software engineers. But other than the difference in their names, how many of us can comprehend the diversity in the work they do?
As I guessed, not many people can guess the job that these data experts are up to. Many of us eventually come to the conclusion that all of them do the same job and are grouped differently for the sake of it. There is nothing more mistaken then this myth and for this purpose, I have turned up as a myth buster today to put an end to the conflict in understanding the role of these jobs present in the data industry. While all of them help propel the movement towards authentic data creation by architecting the growth upwards, there is a major difference in how and why they come into the perspective.
Here I have outlined some of the major attributes of these four subcategories that come in the bigger picture of managing and looking over data. They say ignorance is bliss, but it is always better to know the real picture than to shy away from it.
The statistician sits right at the forefront of the whole process and applies statistical theories to solve numerous practical problems pertaining to a plethora of industries. They have the leverage and the independence to determine the method deemed feasible for finding and collecting data.
Since statisticians are deployed to collect data through meaningful methods, they design surveys, questionnaires, experiments, etc., to collect data.
They analyze and interpret the analyses from the data and report all the conclusions that they find through their analyses to their superiors. Statisticians need to boast of analytic skills along with the ability to interpret data and narrate complex concepts in a simple, understandable manner.
Statisticians understand the numbers that are generated through research, and apply these numbers to real life issues.
A software engineer sits at an important front of the data analytic process and is responsible for building systems and applications. Software engineers will be part of the process of developing and testing/reviewing systems and applications. They are responsible for creating the products that ultimately lead to the creation of the data. Software engineering is probably the oldest one of all these four roles and was an imperative part of society way before the data boom began.
Software engineers are responsible for developing frontend and backend systems that help collect and process data. These web/mobile applications lead to the development of the operation system through a flawless software design. The data that is generated through the apps created by software engineers is then passed on to data engineers and data scientists.
A data engineer is someone who is dedicated towards developing, constructing, testing, and maintaining architectures, such as a large scale processing system or a database. The main difference between a data engineer and its often confused alternative data scientist is that a data scientist is someone who cleans, organizes, and looks over big data.
You might find the use of the verb “cleans” in the comparison above really exotic and inadvertent, but in fact, it has been placed with a purpose that helps reflect the difference between a data engineer and data scientist even more. In general, it can be mentioned that the efforts that both these experts put in are directed towards getting the data in an easy, usable format, but the technicalities and responsibilities that come in between are different for both of them.
Data engineers are responsible for dealing with raw data that is host to numerous machine, human, or instrument errors. The data might contain suspect records and may not even be validated. This data is not only unformatted, but also contains codes that work over specific systems.
This is where data engineers come in. Not only do they come up with methods and techniques to improve data efficiency, quality, and reliability, but they also have to implement these methods. To manage this complication, they will have to employ numerous tools and master a variety of languages. Data engineers actually ensure that the architecture that they work upon is feasible for data scientists to work with. Once they have gone through the initial process, the data engineers will then have to deliver or transfer the data over to the data scientist team.
In simple terminology data engineers ensure the flow of data in an uninterrupted way through servers. They are mainly responsible for the architecture needed by the data.
We now know that data scientists will get data that has already been worked upon by data engineers. The data has been cleaned and manipulated and can be used by data scientists to feed analytic programs that prepare the data for its use in predictive modeling. To build these models, data scientists need to do extensive research and accumulate high volume data from external and internal sources to answer all business needs.
Once data scientists are done with the initial stage of analysis, they have to ensure that the work they do is automated, and that all insights are duly delivered to all key business stakeholders on a routine basis. It is indeed noticeable that the skill set needed for being a data scientist or a data engineer as a matter of fact is slightly similar. But the two are gradually becoming even more distinct within the industry. Data scientists need to know the intricate details related to stats, machine learning, and math to help build a flawless predictive model. Moreover, the data scientist also needs to know details pertaining to distributed computing. Through distributed computing, the data scientist will be able to access the data processed by the engineering team. The data scientist is also responsible for reporting to all business stakeholders, so a focus on visualization is necessary.
Data scientists use their analytical capabilities to find out meaningful extracts from the data that is being fed to the machine. They report the final results to all the key stakeholders.
The field of data is a growing one, and encompasses way more possibilities than what we had imagined before.