“The eternal secret of the world lies in its comprehensibility. The fact that it is comprehensible is a miracle "A. Einstein

Data Science (DS) originates from statistics, which in turn originates from the great works of mathematicians, starting with the work of Arab scientists in the 8th century AD, continuing with the works of scientists of Renaissance and late Renaissance (Isaac Newton, John Graunt, Blaise Pascal, Pierre de Fermat, etc.) and ending in the 19th - 20th centuries (Ronald Fisher, Egon Pearson, etc.)

What is the main difference between lying DS and statistics? The main difference is two things:

1) Historically statistics were limited by computing power, so it was based on the use of data samples. As a result scientists were forced to base their conclusions on the basis of some general population. Modern Data Science has practically no limitations in computing power, so we can abandon the need to carry out statistical sampling and can analyze and draw conclusions based on the entire amount of data.

2) Before the modern high-performance computer’s extension corresponding application of AI and machine learning algorithms, scientists were forced to first put forward some hypothesis and conduct observations in accordance with it. Thus, scientists had to carry out significant work to identify dependencies and confirm the hypothesis put forward, which, given the significant limitations in computing power, did not always turn out to be correct. In today's world, a DS professional can use appropriate algorithms and models to programmatically identify dependencies. The cardinal difference of the modern DS is that a person can entrust almost any routine work to a machine. While a DS specialist is required to understand the basic algorithms, models, libraries and capabilities of information systems.

**Sub conclusion:**

Thus, at present, there are unlimited resources for processing and working with any data. Modern companies, institutes and other large formations have now learned how to accumulate and store large amounts of data, although not always in a structured form. As a result, we see a huge potential for working with data to obtain effective models that allow us to achieve a huge number of goals.

Namely, with modern computing and advanced algorithms, we can:

A) discover hidden trends in large datasets

B) identify opportunities to achieve goals by grouping and clustering data

C) take advantage of trends to predict

D) calculate the probability of any possible outcome

E) get accurate results quickly and with a minimum of human time

If you are interested in deploying a DS Competence Center in your company, then I will be happy to help in the implementation of such projects. Please contact me through my website: http://akonnov.ru/ or through my Telegram channel: https://t.me/biz_in