Organizations in several industries such as banking, healthcare, and automobiles are now acknowledging the value of data science in their mode of operation. Thus, an ideal and efficacious data science team are therefore expected to manage numerous volume of tasks.
Even then, developing a team to successfully manage AI tasks is essential to tackle any challenges faced by organizations as regard data.
While studies over the last few years have shown that data engineering, a vital phase forward to Data Science and AI, it is still a phase that many businesses completely skip, misunderstand, or neglect.
As a result, numerous Data Scientists finish up doing the work of Data Engineers and hardly have any resources or ability to construct useful data science tasks. There is a recurrent misconception on the part of companies when filling up the role of Data Scientists and Data Engineers, resulting in employing the wrong person for a position, thereby confusing the employee.
Nevertheless, as a team the two professions work hand-in-hand for a common goal though they involve using different skillset which lead us into the main focus of this article “is data engineering a must for data scientists?” Let’s look into this points to get a clearer view of the answer.
Intersection of skills
Skills can intersect between the two professions, as they both need to be able to interact and support each other. They should consider each other's decisions and the responsibilities of each other in order to ensure that the partnership is as seamless as possible.
It’s not a must for data scientist to have skill in data engineering before they can analyze data processed by data engineer or before they can move uniformly with other group (involving data engineer) for the progress of the company.
The basic knowledge of data engineering skill
In some cases, Data scientists can use the understanding of the basic knowledge of data engineering needed to prove their point in a project.
For instance, let's say you have a proof of concept, you might not need to build pipeline of data to prove your point.
In the process, you realize that basic knowledge in data engineering can be of help to validate the forecast of your analytics, it will be okay to learn that skill since it will give your idea a clearer picture.
Note that a data scientist cannot perform the function of a data engineer vice versa even with basic knowledge of the skill.
Now, we will be looking into some basic knowledge of engineering that can be of help to data scientists.
Structured Query Language (SQL)
SQL has been in existence for some time now, and database systems are still prevalent and important in many companies. Data scientists should acquire some basic SQL functionality.
Getting a practical knowledge of data processing will also help.
However, Data Scientists are not compensated for creating SQL reviews or enhancing the accuracy of the data in the databases.
Through the growth of Google Cloud, Microsoft Azure, understanding cloud computing has become highly useful to every Data Scientist.
Companies using these technologies will also allow their data science departments to use their cloud technology to build their projects. Conversely, Data Scientists do not have to know how to build the whole system or pipeline on their own; this isn't their role.
However, understanding the pipeline and understanding how to execute the concept in the cloud can be very useful.
For example, creating or implementing powerful machine learning systems can involve clusters, virtual machines, or warehouses. It would then be handy to know how to manage these programs.
The domain of Data Science & Data Engineering is continuously evolving and responding to the issues that arise.
While data scientists should be interested in evolution, they seldom require an advanced understanding of any of the applications or systems listed above, data scientist doesn't need to know how it works and how it's going to be configured, just the basic knowledge will do.
But it is important to accept growth by acquiring the appropriate knowledge.