These<\/em> days, the business world runs entirely on data and none of the companies can survive without data-driven strategic plans and decision making. The field of data science<\/strong> is quite broad and contains a significant number of job positions including data scientist<\/strong> and data engineer<\/strong>. If you want to step into the data science<\/strong> field, it\u2019s crucial to understand the differences between a data scientist<\/strong> and data engineer<\/strong> to identify whether it\u2019d be possible for you switch positions without investing much effort and time.<\/p>\n

In this post, we\u2019ve tried to outline the key differences between these two positions to help you make an informed decision. Let\u2019s start the discussion.<\/p>\n

1- What\u2019s a data scientist?<\/em><\/strong><\/h3>\n

<\/p>\n

Data scientists<\/strong><\/em> are the people who\u2019ve got the ability to derive actionable insights from massive datasets to address specific business problems. At their core, these people analyze massive amounts of data to develop applied mathematical models.<\/p>\n

2- What\u2019s a data engineer?<\/em><\/strong><\/h3>\n

<\/p>\n

A<\/em> data engineer<\/strong> is a professional who focuses on preparing the data infrastructure for analysis. Their job responsibilities encompass the production readiness of data and various other things like resilience, scaling, formats, and security.<\/p>\n

3- Skills required for each position<\/em><\/strong><\/h3>\n

<\/p>\n

At<\/em> their core, data engineers<\/strong> come from a programming background. This usually encompasses Python, Java, or Scala. These people usually have an emphasis on big data and distributed systems.<\/p>\n

Data scientists<\/strong>, on the other hand, usually come from a statistics and\/or applied mathematics background together with computer science. These people also need to interact with different business domain experts to cultivate the desired insights.<\/p>\n

4- Overlapping skills between a data scientist and a data engineer<\/em><\/strong><\/h3>\n

<\/p>\n

There\u2019re<\/em> various skills where both of these positions\u2019 abilities overlap. For instance, both of them overlap on programming. However, a data scientist\u2019s<\/strong> are usually well behind that of a data engineer<\/strong>. They also overlap on analysis. Here, a data scientist\u2019s<\/strong> analytics skills are well beyond the analytics skills of a data engineer<\/strong>. Probably the biggest overlap can be observed when it comes to big data. A data engineer<\/strong> uses his\/her systems creation and programming skills to develop big data pipelines. And a data scientist<\/strong> uses his\/her advanced math and limited programming skills to develop advanced data products utilizing those existing data pipelines.<\/p>\n

At some organizations, data scientists<\/strong> are tasked with doing things that data engineers<\/strong> should. While data scientists<\/strong> aren\u2019t equipped with the skills to become data engineers<\/strong>, they can acquire the skills. On the other hand, it\u2019s far less common when data engineers<\/strong> begin doing data science<\/strong>. In reality, these positions aren\u2019t interchangeable and it may not be completely easy for a data engineer<\/strong> to become a data scientist<\/strong>. However, recently we\u2019re seeing a new breed of engineers who\u2019re proficient in both data science<\/strong> and data engineering. These people have enough experience and knowledge to work in both fields. These people are called machine learning engineers who\u2019re cross-trained to become experts at both fields. As the bar for performing data science<\/strong> is decreasing gradually, we can expect to see the value of these people increasing only.<\/p>\n