As the amount of data grows, there is a greater need to store, analyse and make it useful. With all these needs, various jobs started to emerge in the IT world with different goals in mind. However, for those interested in a career path within data and analytics, choosing between the various related roles can be daunting: data scientist, data engineer, data analyst, data architect. In this scenario, united by data, but separated by different goals, data scientists and data engineers emerge.
A organisation's goals for both positions may sound similar. Sometimes job postings lead to confusion about what exactly a data scientist job or company needs to solve its data problems. Job posting qualifications (such as Python and SQL) may also arise in data science and data engineering, as these two roles are constantly being used and shaped, and the lines between data scientists and data engineers are often blurred.
But data scientists are different from data engineers. Data scientists clean and analyse data, answer questions, and provide metrics to solve business problems. On the other hand, data engineers are responsible for developing, testing, and maintaining the data pipelines and architecture that data scientists use for analysis. They are responsible for helping data scientists provide accurate metrics.
A common joke in the IT world is that a data scientist is better at programming than a statistician, and is better at statistics than a programmer. In other words, it's not just programming, because at the end of the day we still worry about standard deviations or means, and it's not just statistics, because you're never going to ask a statistician to train a Support Vector Machine in Python.
Data science is a mix of tools, theoretical knowledge and the limitations that this entails. When we talk about data science, we can say that 80% is data exploration and 20% is training and testing models. Understanding incoming data, knowing how to manipulate it and “preparing” it is some of the most important skills needed to create a good data analysis model/machine learning model that answers the questions we asked.
A common joke in IT is that data scientists are better at programming than statisticians and better at statistics than programmers. In other words, it's not just programming, because at the end of the day we still worry about standard deviation or mean, and it's not just statistics, because you'd never ask a statistician to train a SVM in Python.
Data science is a mixture of tools, theoretical knowledge, and the limitations that come with it. When we talk about data science, we can say that 80% is data exploration and 20% is training and testing models. Understanding incoming data, knowing how to manipulate it, and "preparing" it are some of the key skills needed to create a good data analysis/machine learning model to answer the questions we ask.
In data science, there is no definitive solution. That's why data scientists have to learn and work with a variety of technologies. For example, there is no clear programming language. Many use Python, but R, Matlab and Java are other viable options with several associated libraries, which will help your work faster and easier (Pandas in Python for example). Finally, as you continue to learn, you will delve into Machine and Deep Learning and here there are also many useful libraries.
Data Engineers are responsible for architecting/designing and developing the data pipelines (batch or streaming/NRT) that will be the backbone of future data-driven organisations, which can take organisations' data from sources to destinations efficiently and properly with the best possible quality. In this way, organisations can leverage their best asset – their data – for operational purposes (such as feeding critical backends that serve as operational foundations) and analytical purposes (to be used by data scientists to extract strategic change insights for organisations, for example). Data engineers should be seen as data facilitators and their ultimate goal should be to easily provide the data for the data scientist (not exclusively) to shine.
Good data engineering can increase the production of data scientists tenfold, providing good and timely data in the best format for the latter to take advantage of. Typically, a data engineer is the go-between in the world of data and is responsible for integrating it across multiple boundaries: technological, political, departmental – this usually makes him a facilitator and system integrator.
A data engineer usually works with highly structured and very efficient programming languages/runtimes (for example Scala, Java), which allow the creation of very fast and robust data processing pipelines. As a data engineer, it most often applies to “glue” that attaches multiple systems (including data science projects) requires proficiency in languages commonly used by data scientists, such as Python.
Despite the differences, data engineers and data scientists need to coexist in the same environment. Because data projects are complex and time-consuming, they require the full input of both. We can share some tools and have overlapping skills, but the tools that define us and our goals are vastly different and very mature. Both add tremendous value to the IT world, but in different ways. While data engineers focus on efficient data processing/handling, movement and storage, data scientists focus on knowledge discovery and data analysis. Future data platforms will be built by Data Engineers and Data Scientists, who should complement each other rather than confuse each other.
Now more than ever, data is shaping the future. Are you ready to contribute your curiosity, imagination, and energy to the world of data science? Data Engineer or Data Scientist? We hope you discover the career that matches you.
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