A data scientist is a professional who deals with a colossal amount of information, analyses it, and helps organisations to derive actionable insights from data. However, with a high median base salary and being the sexiest job of the century, the role of a data scientist is getting a lot of attraction from individuals as well as businesses. In fact, according to Glassdoor research, the average base pay of a data scientist is ₹988K per year, and therefore many professionals are marketing themselves as data scientists despite lacking actual skills with data.
Alongside, the job profile of a data scientist is quite complex, and therefore many of the business leaders don’t understand the core of it. Consequently, many think that any professional who deals with data are data scientists. However, that’s not the case. To be a real data scientist one needs to have much more skill setsapart from just knowing the data. Also, with inadequate skill sets, a so-called data scientist can make ineffective data models, which in turn would affect the company’s bottom line.
In this competitive landscape, thousands of professionals are applying for this highly regarded job, and therefore companies must be able to differentiate a genuine data scientist from the ones that are so-called terming themselves as data professionals.
To be a genuine data scientist one has to have an advanced understanding of unstructured data along with knowledge of working with statistical analysis, which also includes basic concepts like hypothesis, probability analysis, as well as testing. For a real data scientist, working with unstructured data is critical to get a better understanding of statistical analysis and experience in practical data science to solve business problems. In fact, Bill Inmon, also known as the father of data warehousing stated that the majority of data scientists spent their entire careers looking at structured data and focus less on unstructured data. “98% of corporate decisions are made on 10% of the data, and there are many facets to the process of getting value from unstructured data. One facet is actually access to data. Then there is the interpretation of data, and then there is the creation of a database. You have got to do each one correctly.
Along with a high qualification degree in mathematics or statistics, it is also crucial for a data scientist to work and gain mastery in tools and technologies that are required to solve day-to-day business problems. R and Python have been the backbone of data science, and to be called as real data scientists one needs to have solid knowledge on these languages as well as need to have the necessary experience on the same.
Apart from these, there are several tools like SAS, Scala, Matlab, etc., that one needs to be completely aware of to be hired as data scientists in the real world. Genuine data scientists also need to have programming skills that can be applied to the real business setting. Along with these, another important aspect that will define you as a genuine data scientist is the understanding of explainable AI and the ethics related to it. This includes grasping the knowledge of methods and techniques for applying AI. Explainable AI would contrast the concept of the black box and in AI systems and would enable transparency.
