What is Data Science and Why Business Needs it

What is Data Science

Data Science (DS) is an interdisciplinary area at the junction of statistics, mathematics, system analysis and machine learning, which covers all the stages of work with data. It involves the study and analysis of ultra -large arrays of information and is primarily focused on obtaining practical results.

Every day, humanity generates approximately 2.5 quintillion bytes of various data. They are created literally at every click and pushing the page, not to mention watching videos and photos in online services and social networks.

The science of data appeared long before their volumes exceeded all conceivable forecasts. The countdown has been made from 1966, when the committee appeared in the world according to science and technology — Codata. It was created within the framework of the International Council for Science, which aimed at collecting, evaluating, storing and searching for the most important data for solving scientific and technical problems. The committee is working as scientists, professors of large universities and representatives of academies of sciences from several countries, including Russia.

The term Data Science itself was in use in the mid-1970s with the presentation of the Danish informatics scientist Peter Naura. According to his definition, this discipline studies the life cycle of digital data from the appearance to use in other areas of knowledge. However, over time, this definition has become wider and flexible.

In the 2010s, the volumes of data began to grow in exposure. A number of factors played a role from the widespread spread of mobile Internet and the popularity of social networks to universal digitizing services and processes. As a result, the profession of a date-satist quickly turned into one of the most popular and in demand. Back in 2012, journalists called the position of the Date of Sayentist the most attractive work of the 21st century (The Sexiest Job of the XXI Century).

The development of Data Science went along with the implementation of Big Data technology and data analysis. And although these areas often intersect, they should not be confused with each other. All of them suggest an understanding of large arrays of information. But if the data analytics answers questions about the past (for example, about changes in the behavior of customers of any Internet service over the past few years), then Data Science literally looks into the future. DS specialists based on big data can create models that predict what will happen tomorrow. Including predict the demand for certain goods and services.