Data science has many disciplines from which the basis is built on statistics and mathematics that originate from decades of (academic) research and development. Many of the original core algorithms form the fundamentals in disciplines such as text mining, image recognition, sensoring, and time series. In the early days, these methods were published without the accompanying code. To apply the method, companies hired scientific programmers to do the challenging and time-consuming task of method implementation. But before writing a single line of code, there was usually a process of thinking why the effort should be taken, and what kind of results could be expected. Over the last decade, this has changed dramatically because companies such as Google, Meta, etc started open-sourcing their libraries. In addition, communities started developing open-source packages such as sklearn, scipy, and many more. An installation is now just a single line of code.
The data science field is fastly evolving but what does the business need?
Nowadays, scientific programmers have become data scientists. However, something has changed. The business also needs data scientists that can communicate effectively with stakeholders, identify business opportunities, and translate technical insights into actionable recommendations that drive business value. This has led to a new kind of data scientist; the applied data scientist.