The fact that data science is important isn’t breaking news. It comprises developing ways for collecting, recording, storing, and analyzing data in order to extract relevant and not-so-obvious (at first glance) facts. The goal of data science is to collect data from a variety of structured and unstructured sources. Although it is not a descendant of computer science, data science is closely related to it. Data Science is concerned with the entire analytical process, from getting accurate data to developing graphs and reports based on the findings, whereas Informatics is concerned with data processing.

Statistics occupy a very large place in Data Science – it is in this mathematical discipline that the basic concepts are based. Another important component is visualization (i.e., how to present the results of analytics beautifully and clearly). So Data Science is not pure computer science, not clean statistics, and not pure programming. Data Science is something more. We recommend you this data science agency.

If we trace the historical chain of the emergence of data science, the sequence will be something like this: computers appeared, began to collect information from easily accessible sources -> this became not enough, so improved “iron” -> began to extract even more diverse information -> improved collection tools and operational tools -> the growth of information has turned into an avalanche that brings everything in its.

Since 1960, there has been a word known as “data science.” It was, however, first used as a synonym for “informatics.” This phrase was coined about 15 years ago to describe data processing technologies used in a variety of applications (which is closest to what Data Science presents now). Data science became a separate discipline in 2001.

Students frequently bring up the term “data mining” in our Data Science class and compare it to data science. That isn’t exactly correct. Data Mining (also known as intelligent analysis) is a subset of Data Science that involves analyzing large amounts of data to find patterns and extract usable and relevant information.

A simple example: a large company is Data Science, the software department is Data Mining, and the raw materials are Big Data. The support department does its job very well: it concludes supply contracts and contacts suppliers. Its task is to provide the enterprise with all the necessary raw materials so that the production process does not stop. At the same time, the security department itself chooses from whom to order, what conditions to agree to, and so on. But other departments of the company can also purchase the goods they need. They are responsible for product manufacturing, packaging, logistics, marketing, and so on. All of them are independent, but they work for the benefit of one enterprise.

Statistics or forecast analytics use the accumulated data to assess events that may occur in the future.

Data Mining, statistics, machine learning, analytics, and programming are other “departments” of Data Science that are methods for acquiring, processing, and retrieving information, extracting significant data, and interpreting it to make formally reasoned conclusions.

Machine learning is a tool that is designed to intelligently process massive amounts of data in a way that a human cannot. Machine learning algorithms are a huge step forward in the field of information technology. They have the ability to learn from their mistakes (and they do it clearly better than you and I). With each method run, the computer improves the decision-making model.

In other words, it corrects the initial state inherent in it by a specialist so that no such error occurs next time. At the first stage of creating the algorithm, the adjustment takes place with the help of an expert – a person. He tells the car where it made a mistake. This process is called a trial learning process.

Then the machine itself builds models and templates and determines from them whether the choice is made correctly or not. This is an extremely complex technology but effective and convenient. A person will never be able to achieve that accuracy and the volume of data processed. Machine learning algorithms after processing unstructured data produce already structured ones as a result.

Following that, the information requested on some basis is placed in the “paws” of an expert who performs a thorough and multidimensional examination on it. The analyst translates, converts, and organizes data into a single language that the decision-making team can understand. You also can learn more about the achievements of artificial intelligence here.

In a nutshell, Data Science is a powerful science capable of foreseeing the future, explaining the present, and uncovering historical patterns.

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