{"id":3726,"date":"2020-12-15T13:52:00","date_gmt":"2020-12-15T12:52:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/data-scientist-vs-data-engineer\/"},"modified":"2021-09-20T12:55:02","modified_gmt":"2021-09-20T10:55:02","slug":"data-scientist-vs-data-engineer","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-scientist-vs-data-engineer\/","title":{"rendered":"What\u2019s the Difference Between a Data Scientist and a Data Engineer?"},"content":{"rendered":"

In our data-driven economy, new job roles are emerging. Two of these are data scientists and data engineers. But what do they involve? Let\u2019s find out.<\/strong><\/p>\n

Despite only being at the frontier of the information age, it has already spawned a digital revolution. Core to this is big data\u2014the constant stream of information that\u2019s reshaping the way our society and economy work. The existence of big data alone has transformed our shopping habits, our access to healthcare and education, how our businesses are run, and of course, our job market. Two fresh fields in this area are data science and data engineering. But what\u2019s the difference between them, and which, if either, is the right one for you?<\/p>\n

In this post, we\u2019ll look at the differences between data science and data engineering, asking:<\/p>\n

    \n
  1. Data science vs. data engineering: what\u2019s the difference?<\/a><\/li>\n
  2. What are the key skills for data scientists and data engineers?<\/a><\/li>\n
  3. How much do data scientists and data engineers earn?<\/a><\/li>\n
  4. Should you become a data scientist or a data engineer?<\/a><\/li>\n
  5. Key takeaways<\/a><\/li>\n<\/ol>\n

    Ready to learn about two possible new career paths? Read on.<\/p>\n

    1. Data science vs. data engineering: what\u2019s the difference?<\/h2>\n

    Because data science and data engineering are relatively new, related fields, there is sometimes confusion about what distinguishes them. Toss the word \u2018data\u2019 into a job title, and people (at least those who aren\u2019t in the know) tend to lump things in together! In reality, data science and data engineering are two very distinct roles. Let\u2019s explore further.<\/p>\n

    What is data science?<\/h3>\n

    Data science is an interdisciplinary field of scientific study. It focuses on obtaining insights from very large datasets (or \u2018big data\u2019). Data scientists may work in any number of industries, from business to government or the applied sciences. However, all data scientists share a common goal: to analyze information and to obtain insights from that information that are relevant to their field of work.<\/p>\n

    For example, in business, big tech companies often hire data scientists to help them perfect their customer recommendation algorithms (or to tailor the customer experience in other ways). The finance industry uses data science to help inform the creation of new products. In healthcare, big data can be used to diagnose disease. The list goes on and on.<\/p>\n

    Most data scientists start their careers in areas related to math and statistics. They usually then develop into areas like data analytics<\/a> and machine learning. Skills required range from knowledge of computer science to information visualization, communication, and business. However, data scientists also require a great deal of technical knowledge, such as how to apply complex data modeling architectures. This is one area where data science overlaps with data engineering (which we\u2019ll explore later).<\/p>\n

    Increasingly, many data scientists are carving niche careers in very specialized areas. This is possible due to the deluge of data that now impacts every part of our lives. In every industry, the demand for data scientists is growing. This is why data science is considered one of the \u2018sexiest\u2019 careers of the 21st century<\/a>!<\/p>\n

    We’ve covered the basics of data science (and how to become a data scientist) in detail in this article<\/a>.<\/p>\n

    Next up\u2026<\/p>\n

    What is data engineering?<\/h3>\n

    Data engineering (also known as information engineering, or information systems engineering) is a software engineering approach. A data engineer\u2019s job is to build the appropriate software architecture to collect and funnel big data. Others working in the field (including data scientists) can then use these data. While data engineering and data science both involve working with big data, this is largely where the similarities end. Data engineering has a much more specialized focus.<\/p>\n

    A data engineer\u2019s role is to build or unify different aspects of complex systems, taking into account the information required, a business\u2019s goals, and the needs of the end-user. This involves creating highly complex data pipelines. Just like oil pipelines, these data pipelines collect raw, unstructured data from any number of different sources. They then channel them into a single database (or larger structure) where they are stored. While data scientists also source data as part of their role, unlike data engineers, this is not their main focus.<\/p>\n

    Unsurprisingly, data engineers need an in-depth understanding of dozens of big data technologies and how these technologies interact. From beginning to end, a data engineer\u2019s job involves strategic planning, data modeling, designing appropriate systems, and finally, prototyping, constructing, and implementing those systems.<\/p>\n

    Without data, there is no data science. By extension, we need the right structures to collect and store information. This is a particular challenge for older, larger organizations, whose legacy architecture is often insufficient for 21st century needs. That\u2019s why, even though data engineering is not generally considered to be as \u2018hot\u2019 as data science, talented data engineers are highly in demand. If you\u2019re considering a new career, take note!<\/p>\n

    2. What are the key skills for data scientists and data engineers?<\/h2>\n

    OK, so we now have a fairly good understanding of the difference between data scientists and data engineers. Now let\u2019s dive a bit deeper and look at the core skills and responsibilities for each role.<\/p>\n

    Key skills and responsibilities of a data scientist<\/h3>\n

    Most data scientists have backgrounds in areas like mathematics or statistics. Key skills for a data scientist include:<\/p>\n