Dubbed by the Harvard Business Review as the “Sexiest Job of the 21st Century”, data scientists have the tedious and crucial job of converting overwhelmingly large quantities of data into actionable insights. Companies in every industry are employing data scientists in every department from research, sales, customer service, to human resources. With the prolific growth of the data science industry and the increase in demand for such jobs, we observe that the Indian job market is not ready for this paradigm shift.
Skills required to be a prolific data scientist
In order to understand how the Indian skill market falls short, it is necessary to understand the requirements for being a great data scientist. Is it just intelligence and programming skills? Or more?
It goes without saying that programming skills are the most important to any data scientist as languages such as Python or R or database management languages like SQL always act as a reliable foundation for any software development project. Additionally, a command over statistics is another key weapon in the arsenal of any data scientist. Since they are tasked with handling voluminous data, being able to use and manipulate this in order to reveal patterns, trends and analysis are crucial. In fact, as most data is unstructured and omnidirectional with missing values and inconsistent string formatting. Any data scientist needs to be efficient in data wrangling to help overcome these common roadblocks.Skills required to be a data scientist. Credit: Medium.
Other important skills are a panache for calculus and linear algebra which are the foundation for various processes, including machine learning. Companies that are based around data-driven products which need continuous performance or capability optimizations usually deploy machine learning algorithms to improve and add upon self-learning capabilities to technologies.
Finally, perhaps one of the most important skills that distinguish data scientists from statisticians or any other professionals is their ability to visualise the interpreted data and present it to stakeholders. SImply being able to process and interpret data isn’t enough, it is equally as important for data scientists to be able to present this data to management or other stakeholders as this will help drive company strategy whether about sales, production, management or communication.
Huge skill gap prevalent in India
When it comes to the growing importance of data science in almost every industry, it is clear that the Indian job market is not ready for this paradigm shift. When software engineers are hired to be data scientists, they believe being proficient with Python and R would mean an easy transition to being data scientists, however, this is hardly ever the case.
For instance, take the case of a company that wants to develop its own, unique and in-house data management implementation instead of relying on the pre-defined options present in Python libraries. Merely, knowing a programming language will not enable candidates to finish the job.
Current skills of Indian engineers
How to fill this knowledge gap
However, there is still room to improve, and if you are a data science enthusiast, there’s plenty of steps you can take undertake to fill the knowledge gap between the prevalent and requisite skills, starting from the courses you study in college to being willing to take risks.
Perhaps one of the most important ways you can help further your goals of becoming a data scientist is to choose your courses carefully, thereby, making sure that you are specifically focused on subjects of statistics and programming. These courses provide a strong foundation for any data scientists and will ensure your technical skills are up to the demands of the industry.
It is important to remember that learning should never be limited to the classroom or the course you are undertaking. While these are important in helping you have the technical skill set to succeed, taking upon personal projects like building a data-driven application or applying data analysis techniques to unstructured data sets is what will continue to fuel your passion in such a dynamic job market.
Finally, do not be afraid to deskill. If you are at a point in your career that you need to reskill to break past the glass ceiling, do not hesitate. Data science is a new and exciting field with tremendous opportunities and growth potential for individuals. Engineers and enthusiasts alike need to ensure they adapt their skills to this changing and dynamic market to ride this wave of change.
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