In the 21st century, data is of great importance, so it’s no wonder that data science and data engineering have become one of the most popular areas, not just in the IT world. Although data science experts and data engineers share certain characteristics, their professions differ significantly. These occupations cannot be viewed interchangeably as this can have a negative effect, e.g., it can hurt the outcome or lower productivity.
So let’s check what makes data science stand out and how this discipline differs from data engineering services.
Table of Contents
Data engineering is a structured approach to software design, development, and maintenance. Data engineering clearly defines the requirements, making it easier to continue with software development. Data engineers are responsible for building or unifying diverse aspects of complex IT systems based on the required information, the business’s goals, as well as the user’s needs.
Data science is an interdisciplinary field of scientific methods, processes, and systems for concluding information using specific system software and techniques. It concentrates on analyzing large datasets, i.e., big data. Although data analysts may work in various industries, they have one common goal – to extract insights from data in various forms.
Data engineering and data science are two completely different disciplines despite having data as a common ground. Below, we list six crucial differences between them:
Data engineering deals with building architecture to generate data (processing, storage, and retrieval) from various sources. On the other hand, data science deals with cleaning and organizing data. It performs in descriptive statistics and analyses to draw useful insights or solve business needs.
As for the area of expertise that data scientists and data engineering should have, the first one should be an expert in statistics, mathematics, computer science, and domain. In this case, hardware knowledge isn’t necessary and required. If we are talking about a data engineer, this person should have knowledge of programming, hardware, or middleware. Statistic, as well as ML knowledge, isn’t required.
The principal responsibility of data science is the optimization of ML models. The purpose is to prepare data to be used in predictive or prescriptive analysis. On the other hand, data engineering is responsible for improving the performance of the entire data pipeline.
In contrast to data science, data engineers aren’t required to prepare visual/graphical representations or charts from the underlying data.
Data science uses analytical tools, data visualization tools, and database tools. On the other hand, data engineering makes use of design and analysis tools, database tools for software, and programming language tools to connect systems.
A data product is a result of data science. However, the product of data engineering is data storage and retrieval. In general, a data engineer prepares the data upon which a data scientist can develop statistical and ML models.
Most data scientists have a background in statistics or mathematics. Here are some of the skills this professional should have:
In contrast, the skills of a data engineer will relate to knowledge of programming. We can distinguish the following skills here
Despite the differences mentioned above, there is an overlap between those two disciplines.
Both data Scientists and Data engineers share expertise in analysis. However, it should be noted here that a data analyst has much more advanced analytical skills than a data engineer.
Data scientists and data engineers need to be fluent in the following languages: Java, Scala, Python, R, C++, JavaScript, or SQL.
In addition, both share programming skills. In this case, data engineers possess specialized programming skills that set them apart from data scientists.
There is a big difference between data science and data engineering. Each of the fields focuses on a specific problem area and requires specialized skills as well as approaches for dealing with issues. The goal of data engineering isn’t necessarily to develop machine learning or statistical models. But it’s to transform the data so that data scientists may apply machine learning models. Despite developing a core algorithm for visualizing and analyzing the data, data scientists require enriched and processed data from data engineers.
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