As discussed above, there is a huge abundance of data. However, there aren’t enough resources to convert this data into useful products.
That is, there aren’t enough people who possess the required skills to help companies utilize the potential that data holds. Due to this reason, there is a dearth in the supply of Data Scientists.
Much of this is contributed by the infancy of Data Science as a field. There is a lack of ‘data-literacy’ in the market. In order to fill this vacuum in supply, you need to learn Data Science and its underlying fields.
Data Science is not a standalone field. It is comprised of several sub-fields. These subfields are Statistics, Mathematics, Computer Science and Core Knowledge. Data Science offers a steep learning curve and is difficult to master.
However, with the right resources and direction, one can undertake the journey of mastering Data Science.
A great data science product is like a meal composed of data as its raw ingredient, tools like programming languages used to cook the meal and the foundational knowledge of statistics & math as its recipe
2.1 How to Cover the Skill-Gap?
To assuage the high demand, people should direct their attention towards learning the necessary skills that will help them to take up Data Science as a prospective career. While there are many resources and books on the internet, it is impossible to digest everything all together.
Therefore, people must curate a path and do away with all the necessary clutter to have practical insights about Data Science.
To create a refined data product, we need refined and polished skills. This comes with a combination of knowledge and experience. Since Data Science is a recent field and therefore experience can take a back seat. However, it is built upon existing knowledge of math and statistics that one must know about.
Data Science is about the implementation of this knowledge through several tools and programming languages. Therefore, one must also possess the skills of a computer scientist. Data Science in simple words can be termed as applied statistics without computer science.
Proficiency of these tools is a must since you need to express your knowledge in the right manner. It is, therefore, an utmost necessity to learn all the subfields of Data Science in order to grasp the trends hiding in the data.
There is a pressing need to fill the skill gap in order to churn out Data Scientists required by industries for their versatile applications. Therefore, we can best conclude that learning Data Science is not just about one topic but a collection of various topics ranging from Statistics to Computer Science.
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