So what is Data Science and Why Do We Need It?

Currently, the internet is teeming with job posts looking for talented data scientists. Moreover, there are many educational institutions, such as universities and schools that offer a wide range of data science courses. You can easily find multiple articles and books on this topic that you can easily drown yourself in. What is more, such corporate and renowned giants as е, to name just a few, consider Data Science as a primary source of moving their products forward with it. So what is so special about it, and why is everyone so hyped?

Why is Data Important?

Everything started with data about the world. For example, educated people living during the Medieval Ages, mostly those who resided in monasteries, were considered to be people of the highest intelligence. They possessed information and knowledge on mathematics, nature, and the philosophical study of the physical world and universe. In the Era of Enlightenment, there were smarter people who had a deep and profound knowledge of science and technology. Thus, data is crucially important. The accumulation of data from every period of human history made it possible to create a modern and advanced world in which we live today.

Moreover, we interact with the new kind of world ̶ the digital one. We use social media, purchase things on the internet, study online, and do other stuff using our gadgets on a daily basis. Thus, we create new and massive chunks of data. That is why it is possible to say that we live in the age of Big Data.

What is Big Data?

Simply speaking, Big Data is large and massive sets of various structured and unstructured information which move with the neck-breaking speed, and can’t be properly processed and analyzed with the help of traditional methods and technology.

This is particularly true for the corporate world. In the digital environment of Big Data, companies get humongous volumes of data from their users and clients. They have been accumulating it on their customers: user behavior, users’ social and cultural background, their various preferences in food, clothes, entertainment; their political views, and purchasing history, and other equally personal things.

This thing is true for all companies regardless of the field they operate in and the countries they work with. But still, companies didn’t know, and many still don’t know how to use the data they have gathered to benefit both ̶ their business and clients.

That’s when Data Science and Data Scientists come to the rescue.

What are Data Science and Data Scientists?

To put it simply, Data Science is a cross-discipline mix of scientific fields that encompasses various purely scientific processes, methods, algorithm development, sophisticated technologies, data inference, and elaborate systems to yield significant and real insights from its core ̶ data.

Data Science is called a cross-discipline study because it uses many methods, theories, and practices form different fields, such as mathematics, information science, computer science, and many others. It also uses machine learning, data analysis, statistics to draw valid conclusions from various data.

So, it is not difficult to guess that a Data Scientist is the person who is an expert in data analytics who has the right technical knowledge, skills, and educational background to solve complicated tasks.

That is why Data Science and Data Scientists are essential to companies as they can provide them with valuable insights and information that will benefit their business.

Why do We Need Data Science?

Still, “to yield or extract insights from data” can be quite a vague explanation of why Data Science is important. Data Science can answer a lot of real-life questions that many companies face:

  • Who should we sell our products and services?
  • Why is this product not popular?
  • How many new users will we have next month, year, etc.?
  • Which functions will make our website more user-friendly?

The answers which are given to these and many other different questions with the help of Data Science are tremendously beneficial to businesses. It gives companies competitive advantages over their competitors, provides better user experience and satisfaction, and it can make your company the richest and the most successful corporation out there.

What Are Successful Examples of Data Science?

Data Science can boast real success stories. For instance, who doesn’t like binge-watching mesmerizing and spectacular Netflix TV shows and movies? Currently, it is one of the biggest and most successful companies in the entertainment industry. Netflix has more than 147 million subscribers in over 190 countries worldwide, and its revenue is more than 15 billion USD (as of 2018).

So what makes it so successful? The answer is simple – Data Science. Netflix has an effective recommendation system. If you are watching Stranger Things, it might recommend you watch Altered Carbon next, because Netflix just knows what you like. Thus, by analyzing your preferences, needs, and likings, the recommendation system will suggest the movies and series that you will definitely take a shine to and watch. By doing this, Netflix provides its users with the best customer experience that can cover the specific needs of a particular user.

Another company that successfully users Data Science is Facebook. The social media platform uses Data Science to improve the user experience and help companies to target the right audience. Thus, by analyzing demographic data, user behavior, geo locations, interests, Facebook can show its users targeted ads.

These examples are only a tiny fraction of what Data Science has done for other companies.

Wrapping up

As you can see, with the accumulation of large chunks of data, it’s impossible for Data Science to lose its popularity.

On the contrary, it will become an integral part of every business, so data scientists will become more and more in high demand If you are looking to boost your knowledge in this field, we are ready to help with all your endeavors.
Contact us, and we will provide you with information.

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