Importance of Data Science in 2022

Importance of Data Science in 2022

Why Data Science is important in future? Data is one of the important features of every organization because it helps business leaders to make decisions.

More and more new professions appear in the world. Already today you can meet biohackers or virtual reality designers. Another promising area is Data Science.

Introduction:

This is a profession that combines the features of a mathematician, programmer, engineer, and scientist. Also, The essence of his work is to teach the computer to solve problems that the state, business, or individuals face daily, and the safety of people, the profits of companies, and even satisfaction from evening leisure depend on the quality of their solution. You can learn more about it here – Data Science UA.

Imagine that there are several dozen international airports in the country, hundreds of flights of different airlines arrive every day. Hundreds of thousands of people across the border every day. 

The state, represented by its border service, needs to decide who is allowed into the country and who is not. It is interested in foreign tourists who will go to theaters, restaurants, and excursions; but does not want to see international criminals or persons who have previously violated local laws.

Thus, the state must decide whether to let a person into the country or not on the basis of the passport presented to him and historical data about him. 

Data Science and insurance company:

Another example is the work of an insurance company. For each client, it is necessary to assess the probability of an insured event, followed by determining the cost of insurance based on a variety of data about a person: age, the current state of health and the presence of chronic diseases, lifestyle, bad habits, extreme sports, fines for traffic violations, etc. 

Finally, the third example: we came home from work in the evening; decided to go through our personal mail and watch some good movie. 

You discover a letter from an unknown sender. Spam or not? And how to determine which movie to watch tonight with a girl? And which wine is better to choose?

Data Science and Robot technology:

A data scientist is needed to teach a computer to solve all these and many other tasks. And as soon as the computer starts to perform such processes, then everything happens according to the principle “robots work hard – a happy person.” 

It automatically makes a decision on issuing a loan, trades on the stock exchange; build the best routes, recommends movies and music, focusing on the user’s preferences. 

But of course, before a computer can start solving such complex problems; it must first be trained with data and algorithms. This is the job of a data scientist.

It’s one thing when a company already has well-established processes, ready-made historical data; and even some kind of regular working algorithm. In this case, the data scientist is required; for example, to improve the accuracy of recognition or prediction of an already working system. However, things are not so simple in the modern world. Both tech giants and startups work in the field of innovation. This is not only a field of great opportunities but also great risks.

The process of creating innovations is characterized by a high degree of uncertainty. Here, as a rule, there are no ready-made data, no algorithms, no recipes for success. There is only enthusiasm and sheer vagueness. Accordingly, the approach to what a data scientist should know and be able to do is also changing. P value data science we recommend you this services.

Conclusion:

Data scientist is well versed in the subject area and knows its features; determines and agrees on the technical requirements for the algorithm; finds out (and often creates) the business logic of the application; which requires an understanding of the company’s business processes and the ability to find a common language with completely different people,; who can stay in different parts of our planet: programmers, analysts, managers, marketers, management and the customer.

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