An attempt to understand what is data science, with examples

Data science ensures a safe passage through the most tumultuous of times. Especially now, when the time is precarious and unpredictable. All the major economies around the world have gone cold due to the sudden emergence of COVID19 and the successive lockdowns. Thus, all the commercial and public sector entities are relying more and more on data to bring efficiency to day-to-day processes. And in the near future, the dependence on data is expected to increase due to the ever-increasing demand for more efficient services. The history of humanity is all about big changes. And data science is arming the institutions with the power of predictions so that they embrace the changes with ease and without much risk. Data scientists in our times are the most valuable assets for any venture and can be deployed in a plethora of fields under a variety of circumstances. Understanding data science from a practical standpoint requires a deep dive into the examples of its real-world implementations. Thus this article will try to explain what is data science with examples of its real-world implementations.

In commerce


The times we are living in are witnessing an abundance of all kinds of data. We, during our day-to-day activities, generate a lot of data by utilizing our devices that are connected to the internet. Just a couple of years ago, this data was unusable due to the sheer size and lack of adept and dedicated data professionals. Now we can, and in marketing the effects are visible. A marketing team operating in 2022 can easily assess the purchase and investment data of entire populations and come up with predictions. These predictions are usually concerned with identifying the most prominent individuals, who are in need of a product or service and are willing to invest in the same. Thus the marketing campaigns in our times are precise and are known to be more successful than ever.


The end-user feedback data can help in understanding the needs and expectations of a user population. A data scientist utilizes this data and tries to get in touch with the customers by addressing their needs in a future upgrade.  These upgrades, however, can not be made just like that. They need a lot of internal and feedback data to plan and implement. More the data and more care is taken by a data analyst the product is expected to be more aligned with the needs of a customer and thus the upgrade can be considered successful.


Business forecasting involves a careful assessment of internal and divisional data. So that the limitations and capabilities of a team can be understood. A business analyst at the helm of such operations must understand the operations of an institute from the inside out. Planning based on the demands and markets can never be protected from unwanted failures on the institution’s behalf. As planning involves the execution of the same and before a plan is made and implemented the ability to carry out the same must be assessed in order to avoid a halfway breakdown. In addition to that, the representations and instructions must be transmitted down to an individual level, so that everyone is aware of what is going on and their roles in the bigger picture.

The public sectors

Disaster management

The disaster management sectors are the most benefited among all the sectors that are using data for saving lives. A natural calamity, if it is routine and yearly, can be predicted in detail a long time before the onset. Thus an at-risk population from a calamity can be evacuated with ease. And millions can be saved in the form of human and financial resources. The best example of this kind of data utilization is the eastern coast of America. The coasts of the USA suffer from yearly storms in the summer and the storms are known to affect human and financial resources.


In the healthcare sector,  data is being used on a massive scale for the development of personalized medicine. The healthcare sectors are a huge store of data. And just a couple of years ago we could not handle and process this data. But now, the same data is saving millions of lives through precisely planned therapies. In addition to that, automated histological diagnostic tools and automated prescription tools are being developed all the time using medico-historical data. The future of healthcare sectors all around the world is more automated and efficient. And with time the data dependency is also expected to increase.

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