It is known that today’s digital world runs on data. So the importance of data science is increasing rapidly day by day. Data science aims to find patterns and insights within data (structured and unstructured). Data science plays a vital role in every organization as it can help them monitor, manage, and collect performance measures to improve decision-making.
It also helps them to interpret data and solve complex problems using expertise in a huge variety of data niches. Data science can provide a new and advanced model for thinking about the information world. It results in the increased demand for data professionals like data engineers and data scientists who have appropriate data engineer training.
In this article, we will find out whether a data engineer can become a good data scientist as they both play very important roles in data-related issues. But in earlier times, data scientists were expected and forced to work as data engineers too. As the field of data has grown where an enormous amount of data is being produced, and also data management and gathering become more complex, businesses and organizations require more insights and answers from the data collected.
It results in a partition of a data scientist job role with a new job title as a data engineer. But now, there is a huge difference between their skills, roles, and responsibilities to perform their jobs role and achieve their goals. Let us know about them separately.
Who are Data Engineers and Data Scientists?
Let us know first about data engineers and data scientists, in short.
Data Engineer- Data engineers are professional IT workers who gather and prepare data for analytical and functional use. Basically, they are responsible for creating data pipelines to collect valuable information from different sources and systems.
They work in many different settings to create systems that gather, manage and prepare raw data into usable information for data scientists and business analysts to interpret. They also make data accessible so that organizations can use it to evaluate and optimize their performances.
In other simple words, data engineers work for the fundamental process of data that can be used further by data scientists. They are responsible for planning, designing, maintaining, and optimizing data infrastructure for data collection, transformation, management, and access. They can solve several data-related problems and can experiment with large datasets. They also understand patterns and trends to get real insights.
Data Scientists- Data scientists are high-level professionals who are known as analytical experts. They implement their knowledge and expertise in the technological field and social science to manage data and get meaningful insights. They use data provided by data engineers, to understand and interpret phenomena around them to make better data-driven business decisions.
Their work must be intellectually challenging and analytically satisfying that can keep you close to the new advance in technology. To achieve business goals, they build algorithms and data models to predict outcomes. They can also use machine learning tools/techniques to improve the quality of data or product offerings. They also guide other teams and senior staff through their recommendations. Data scientists perform data analysis by deploying some of the important data tools like R, Python, SQL, SAS, etc. data scientists can also develop predictive models for theorizing and forecasting processes.
They also can use their industry knowledge, skepticism of existing assumptions, and contextual understanding to find better solutions for complex challenges. Data scientists are also known as big data wranglers who can involve statistics, computer science, and mathematics together in their data performance. Therefore they stay on top of innovations in the data science field.
Can a Data Engineer Become a Good Data Scientist?
To find out the answer to the most popular question in the world of data science, let us check the major difference at each level of the role of data engineers and data scientists. We know that the entire business and industrial world run on data. So they need data-driven strategic plans and decision-making.
Now everyone knows that the world of data science is a vast space where several job roles such as data engineers and data scientists are waiting to be filled. Many people or professionals want to switch their job from data engineers to data scientists and vice versa. But before switching, it is quite necessary to understand the major difference between both the job roles.
- Job role- Data scientists have the ability to derive actionable insights from a huge amount of data sets to find out and address business problems. They can also develop applied mathematical models. On the other side, data engineers prepare the data infrastructure for the data analytical process. They are responsible for the production readiness of data, scaling, resilience, format, security, and many other processes.
Data engineers collect all types of relevant data and transform it into pipelines for the data science team. Now data scientists analyze these datasets, test, optimize, aggregate the data and present it finally for the company’s further use.
- Skills for both Positions- As per their job role, Data engineers have a programming background and have sound knowledge of Java, Scala, and Python. They also have an emphasis on distributed systems and big data.
Data scientists usually have mathematical and statistical backgrounds along with computer science. They interact with specific business domain experts to find out desired insights.
- Overlapping Skills- It is observed that these are mane stages where both professionals’ abilities overlap. This gap or overlap can be seen in programming. It also can be seen in the analysis as a data scientist’s analytics skills are well ahead of the analytics skills of a data engineer. In fact, the biggest overlap of skills can be seen for big data, where a data engineer can use system creation and programming skills to create big data pipelines. On the other side, data scientists use advanced mathematics and some programming skills to create advanced data products using pipelines provided by data engineers.
So on the basis of the above-mentioned facts and features, it’s far less seen when data engineers begin doing data science as these positions are not interchangeable, and it may be difficult for a data engineer to become a good data scientist.
