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Data analysts, data engineers and data scientists
There are data analysts, scientists, engineers, and others involved in dealing with data, and even AI. Let's read about how their work is similar and what's different.
Data analysts, data engineers and data scientists
There are large types of experts who deal with data in the big data and in the work phase of artificial intelligence, largely composed of data engineers, analysts and data scientists.
Today, we’re going to look at the characteristics and differences of each profession and what works in practice produce significant results.
◼ What exactly do you do?
One of the areas that people who want to pursue in the field of data analysis are most concerned about is that the clear distinction between data analysis used in the industry is ambiguous.
This happens because of the different ways of analyzing, processing, and utilizing “data” in various specialized fields such as financial, solution service, and marketing agencies.
There are different categories of expertise, including data analysts, data engineers, and data scientists, but the boundaries are often blurry depending on the size of the business and organization.
And since the field of data processing itself is growing relatively recently, the reason is that there is no clear threshold for making a clear distinction.
Nevertheless, there is a clear distinction that most experts agree with. What do you actually do for a living? If you look at it specifically.
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Data analysts: Support business decisions. Based on accumulated data, they help decision makers and assist them in making better decisions. More focused on visualization/communication and business perspective.
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Data Engineer (Developer): More attention is focused on the ability to extract raw data and the parts are are needed. Rather than analyzing the data or producing meaningful results, they strive to find data that is actually available.
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Data scientist: Focuses on fundamental areas such as accumulated analytical algorithms, technologies, and system performance improvement in practice compared to analysts. They use machine learning and deep learning to further analyze accurate data.
(Roles and responsibilities for each position viewed in tabular form)
(Illustration of three positions centered on characteristics. Source: edureka)
◼ Data scientists and analysts
If you’re good at reading things, you’ll find that among the three areas we’ve compared, there’s a lot of similarities in the roles of data analysts and scientists.
Both occupations are responsible for analyzing the collected data and establishing necessary strategies and plans based on it. Therefore, experts are divided on a variety of topics, such as how to view the boundaries between analysts and scientists, and whether the higher level of data analysts is a data scientist.
Comment 1 : Data Scientist ≒ Data Analyst + Data Engineer
One opinion is that data scientists are a high-level professional group that can simultaneously play both the ability to analyze data and the development role based on coding capabilities.
In fact, most of the data workers overseas express this opinion. According to LinkedIn’s profile survey, the percentage of data scientists calling themselves data scientists is about three times higher than that of data analysts, so there are many implicitly agreeing with comment No. 1.
(Data scientists claim to be an intersection of development and analysis)
Comment 2 : Data Scientist ≠ Data Analyst
On the other hand, there are opinions that the roles of data scientists and analysts should be clearly distinguished.
The opinion-claiming position claims that the value or importance of the job is overestimated or devalued by focusing on topics and words that are popular with the times.
The core work of data analysts focuses more on connecting business decisions to instantaneously obtain actionable insights, a quality that is distinct from the role of data scientists.
(Data utilization has a common area, but there are distinct differences from unique areas.)
◼ For the ultimate goal of leveraging data, leverage DS2.ai
While some of the data utilization jobs do not have clear criteria for broad and accepted scope, the ultimate goal must be to utilize the accumulated data to make the necessary judgments and actions.
At DS2.ai, we focus on helping you with the roles you consider necessary for each job rather than the difference in your job. It provides functions in a way that can save the hard work of data professionals who are working in three key tasks.
For example, when a data engineer wants to develop an AI model, he/she can utilize Click AI with quality data used previously, and provide a server environment where artificial intelligence learns and distributes through Skyhub AI.
(Features provided by Click AI Solutions for Engineer Modeling)
(Features provided by Skyhub AI solutions for model deployment and operation)
To help increase data utilization for data analysts and scientists, use Click AI solutions' AutoML utilization - Feature Importance features and prescription analysis capabilities to map out the value of uploaded data and how it can be utilized in the future to help make decisions intuitively.
(Feature Importance feature that shows the importance of the collected data in schematic form)
(Prescriptive analysis that helps you make decisions by showing data flow and results in various formats)
Summary
Today, we looked at three representative jobs in data utilization and the benefits of using DS2.ai.
It may be obvious, but as it is an area of high interest and potential for development, it is an issue that penetrates the core regardless of the job to make sure that one can firmly take root around one’s abilities in the future. DSLab Global will also continue to provide quality solutions to positively contribute to data utilization:D
We’ll be back with another topic later!
References : Science On, Swaastick Kumar Singh, Edureka Blog
Visit Click AI and Skyhub AI to help data engineers, analysts, and scientists work seamlessly, and learn more!