Data scientists, data engineers, and data analysts are among the most in-demand positions right now. However, there is a widespread misconception among those not involved in the sector that they all do the same thing: analyze data and extract useful information for organizations.

This is far from the case. Both a data engineer and scientist have fundamentally separate roles in an organization; however, some overlap is possible.

If you’re new to analytics and aren’t sure which path to take—data science or engineering—understanding the skillsets and results necessary for each can help you make an educated selection.

Let’s learn the differences between data engineers and data scientists, as well as their job profiles and required skill sets.

What is the Difference Between Data Science and Data Engineering?

Data Science VS Data Engineering- Two Peas in a Pod

Before we go into the difference between Data Engineers and Data Scientists, let’s look at what they have in common. The educational history of Data Engineers and Data Scientists is an essential point of comparison between their profiles.

Both professions usually have a background in mathematics, physics, computer science, information science, or computer engineering.

These are the most popular study fields for Data Science career profiles. Data Scientists and Data Engineers are both competent programmers who are fluent in languages such as Java, Scala, Python, R, C++, JavaScript, SQL, and Julia.

Data Science VS Data Engineering- Job Profile

Data scientists are known as those who study data construct algorithms and make conclusions based on that evidence. This position is a step up from a data analyst who cleans and organizes data. Most data scientists’ time is spent developing, testing, and fine-tuning machine learning algorithms.

Data engineers create the data pipelines that data scientists use to acquire and transmit data. They play an entirely separate role in that they’re more concerned with creating platforms and assisting data science work than with analyzing data. However, data engineers must have a solid grasp of data scientists’ work in order to create top-notch data pipelines.

Data Science VS Data Engineering- Skill Set

Most data scientists have a mathematical or statistical background. The following are essential skills for a data scientist:

  • Advanced mathematics & statistics, including a Ph.D. or master’s degree
  • Domain knowledge or skill in the same subject
  • Tremendous business sense.
  • Advanced analytics abilities, such as understanding of prognostic, diagnosis, or sentiment analytical models, and so on
  • Expertise in machine learning and artificial intelligence strategies is also a must.
  • Solid knowledge of big data tools such as Apache Spark, Hadoop, SQL, and others
  • A programming language, such as Python, R, JavaScript, or C++
  • Excellent conceptualization, presentation, and reporting skills (multimedia reports, dashboards, and presentations, for example)
  • Data scientists with specialized knowledge and skills, such as those in the banking business, will have industry-specific information and abilities.

A data engineer’s skills focus more on the essential know-how because their work is more centered on software architecture. The following are some of the most critical skills of a data engineer:

  • Advanced programming knowledge in languages such as Java, Scala, and Python
  • Knowledge of Distributed Computing
  • Database systems knowledge, such as SQL, NoSQL, object-oriented databases, etc.
  • Expertise with dozens of big data technologies, such as AWS, Spark, Hadoop, Hive, and Kafka (and others in the Apache big data ecosystem)
  • The ability to comprehend and connect various frameworks, as well as create appropriate data pipelines
  • ETL (Extract, Transfer, Load) tool knowledge, which is used for merging data from multiple sources)
  • Application programming interfaces (APIs), which are used to connect multiple software programs

In Conclusion:

Data engineers are educated to take algorithms produced by data scientists and put them into a production workflow, unlike data scientists who take a much more research-focused approach. You will not be able to design a data pipeline to ensure that your AI apps reach the end-user unless you have these experts on your team.

If you are planning to integrate the skills of data scientists and engineers, you can contact us at SG Analytics. We provide data science services and data engineering consulting services to help your business grow tremendously. So, what are you waiting for?

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