Data Engineering & Data Science: How Are They Different?

March 29, 2023 - Aptaworks

In the past few years, data has undoubtedly become one of the most in-demand sectors in IT. With the various terms used to define different functions within the data world – from data science, data engineering, data analytics, to machine learning – it comes as no surprise that many users – within IT or business – still have some trouble differentiating those terms. 

If you read through a few data engineer and data science job listings, you might even notice that there are overlaps between the skills demanded for each position. So, where do data engineering and data science overlap and how do they differ?

 

Definitions & Responsibilities 

Data engineering refers to the process of designing and building a system that defines how raw data is collected, stored, and used for analysis.  

On the other hand, data science is an interdisciplinary field that combines mathematics, statistics, artificial intelligence, and computer engineering to identify patterns and extract valuable insights to aid in decision-making. 

From the above definitions, we can see that data engineer is the one responsible for building data architectures and preparing data for analysis, while a data scientist is the one responsible for analyzing data and converting them into actionable insights and predictions. 

 

 

Data Engineering vs Data Science Skills 

Relevant skills and experience for data engineering: 

  • Proficiency in SQL and other database technologies 
  • Knowledge of programming languages such as Python, Java, or Scala 
  • Understanding of data modeling and schema design 
  • Familiarity with data warehousing and ETL (Extract, Transform, Load) processes 
  • Knowledge of distributed computing systems like Hadoop, Spark, or Kafka 
  • Familiarity with cloud platforms like AWS, Azure, or GCP 

Relevant skills and experience for data science: 

  • Expertise in statistical analysis and modeling 
  • Familiarity with machine learning algorithms and frameworks such as TensorFlow, Keras, or Scikit-learn 
  • Proficiency in programming languages such as Python or R
  • Experience with data visualization tools such as Tableau or PowerBI 
  • Understanding of data mining and data cleaning techniques 
  • Knowledge of deep learning and neural networks 

 

Data Engineering vs Data Science Tools  

As both data engineers and data scientists work with company data, there are some overlap in the tools they use. Check out the tools that are generally used for each role! 

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