What is the data science history? Can you elaborate?

“Data Science” was once created in the early Sixties to describe a new career that would help them grasp and interpret the giant quantities of facts that were once being gathered at the time. (At the time, there was once no way of predicting the sincerely large amounts of statistics over the subsequent fifty years.) Data science continues to evolve as self-discipline uses laptop science and statistical methodology to make valuable predictions and obtain insights into various fields. While Data Science is used in areas such as astronomy and medicine, it is additionally used in commercial enterprise to assist make smarter decisions. Statistics, and the use of statistical models, are deeply rooted in the subject of Data Science. Data Science began with statistics and has advanced to include concepts/practices such as synthetic intelligence, computer learning, and the Internet of Things, to name a few. As more significant and extra facts have become available through recorded purchasing behaviors and trends, agencies have gathered and stored them in more substantial amounts. With the increase of the Internet, the Internet of Things, and the exponential rise in information volumes accessible to enterprises, there has been a flood of new facts or massive data. Onc had opened the doorways through groups in search of to amplify income and power higher choice making, the use of massive records started out being utilized in different fields, such as medicine, engineering, and social sciences.

Let’s jump to its definition first before getting to its Data science history . Data science is a methodology of processing different types of data and converting such data into a meaningful and structured format. This field is mostly used in Statistics. The data can be available in different formats like text, images, videos, audio, etc. Firstly after the data is collected it is in the raw format. Data science is such a technique of converting data from raw to fully finished and accurate data.
Data science history can be dated back to 1962 when John Tuckey described this field which he called “data analysis”, which bears relation with modern data science. There has been a lot of fuzz among people about data science history.
In 1985, in a lecture given to the Chinese Academy of Sciences at Beijing in China, C.F. JEFF Wu for the first time used the term “data science” which would act as an alternative for statistics. Later, in 1992 statistics symposium at the University of Montpellier II acknowledged the emergence of a new field focused on data of various origins and forms, combining established concepts and theories of statistics,data analysis along with data science history and computing.
The term data science history has been dated back to 1974, when Peter Naur proposed it as computer science. The International Federation of Classification Societies became the first association to specifically feature data science as a particular topic in 1996.
After the 1985 lecture given by C.F. Jeff Wu in the Chinese Academy of Sciences in Beijing, in 1997, He again suggested that statistics should be renamed to data science. He clearly mentioned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting or limited to describing data. In 1998, Hayashi Chikio stated data science as a new, interdisciplinary concept, with three aspects: data collection, analysis and design
During the 1990s, two popular terms for the process of finding patterns in datasets (which were increasingly large) included “data mining” and “knowledge discovery” .
The title of “data scientist” has been assigned to Jeff ad DJ Patil in 2008. Though it was used by the National Science Board in their 2005 report “Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century”, it referred to any key role in managing a digital data collection.
There is still no correct figure on the definition of data science, and it is considered by some to be a buzzword.Big data is a related marketing term. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.
I guess Now you have a better understanding about data science history.