Data science is difficult to define in a monolithic manner. It can be described as an interdisciplinary field of business analysis which integrates computer science, modeling software, mathematics and statistics and can be applied to any area that generates large amounts of data in need of compiling and comprehending.
The primary concept of data science involves the use of automated tools in the analysis of big data. It can be applied to any big data generating field, right from population genetics and robotics in the science sector to finance and urban development in the socio-economic sector.
Due to its capacity to be applied to almost every field, it requires professionals who are skilled in using the automated data science tools as well as be competent enough to develop new tools and be creative enough to apply the tools to different fields. Usually, professionals possessing a computer science, mathematics, or statistics degree are well positioned to upgrade into a data scientist but any person with a basic degree can attempt to become a data scientist since data is being generated in almost every field and people with automation skills are needed.
Here a question arises about why they would wish to upgrade into a data scientist. A traditional mathematician or a statistician has only a few opportunities to apply their knowledge and skill in another field and for the most part, are constrained to work within their own field. But if a mathematician or a statistician were to acquire skills in programming and knowledge in operating automated data analysis tools, then their employability rises substantially along with their pay. In other words, there is a high demand for highly skilled people in the 21st-century big data world.
A person may have any basic degree but must have all of the following skills in order to be considered a data scientist. The skills are:
- statistics/statistical modeling
- machine learning
- data visualization & interpretation
- business acumen
More related skills the person acquires, the more he/she is versatile. An example representing how knowledge of data science skills makes one's career path more lucrative than before is given below.
The above picture represents an example of an imaginary person holding two degrees in their core field of public health. As we can see, without data science skills the public health professional will mostly be working in their own field without much chance to grow in terms of either salary or improve how their field functions.
As you can see from the above picture, combining data science tools like programming and data visualization enabled our imaginary healthcare professional to become an asset to his/her field. Now the person is not just limited to providing public healthcare but can also improve the way it functions and provide better solutions to problems. This is more rewarding in terms of personal growth, career interests, and salary.
Hence any professional, upon acquiring some data science skills/tools is able to create lucrative opportunities within his/her field as well as create new opportunities outside his/field or integrate with another field. This shows how data science is a lucrative option for any profession.