Data science which emerged as a powerful tool for the 21st century has a rich history to look back to. Let us look back at the history of data science and how it had evolved over the years through this post.
Brief History of Data Science
The term "Data Science" was coined in the early 1960s to represent a new profession that would help people understand and interpret the massive volumes of data that were being collected at the time. At the time, no one could have predicted the truly vast volumes of data that would be generated over the next fifty years.
Data science is a discipline that applies computer science and statistical methods to create meaningful predictions and obtain insights into a variety of industries. Data Science is utilized in a variety of fields, like astronomy and health, but it is also employed in business to assist make better decisions.
Let us dissect the timeline to have a better grasp of the history of data science
Timeline denoting the History of Data Science
1962: John W. Tukey said in "The Future of Data Analysis" in 1962. "The bright American mathematician John Tukey is universally recognized as the first milestone in the history of data science." Although John Tukey has had a huge impact in statistical terms, his most famous coinage is connected to computer technology. Indeed, he is credited with being the first to coin the term "bit" as an abbreviation of "binary digit."
1974: Peter Naur wrote the Concise Survey of Computer Methods in 1974, which looked at data processing methods in a range of applications. It becomes evident as he defines "data science": "The science of dealing with data once it has been established, but the data's relationship to what it represents is assigned to other subjects and sciences."
1977: This year was marked by the foundation of The IASC (International Association for Statistical Computing)
1989: The inaugural Knowledge Discovery in Databases (KDD) workshop was organized and chaired by Gregory Piatetsky-Shapiro.
1994: BusinessWeek featured a cover story on "Database Marketing".
1996: For the first time, the phrase "data science" is incorporated into the title of a conference organized by the International Federation of Classification Societies (IFCS) ("Data science, classification, and related approaches"). "From Data Mining to Knowledge Discovery in Databases," by Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, was also published the same year.
1997: Jeff Wu proposed that statistics be renamed "data science" and statisticians be renamed "data scientists" during his first speech as the H. C. Carver Chair in Statistics at the University of Michigan.
2001: This year was marked by the development of Software-as-a-Service (SaaS). This was a forerunner to the use of cloud-based apps. William S. Cleveland also outlined strategies for preparing data scientists to meet future demands.
2002: The Data Science Journal was launched in 2002 by the International Council for Science's Committee on Data for Science and Technology, a magazine focused on themes such as data system definition, internet publication, applications, and legal difficulties.
2008: The term "data scientist" became a buzzword in 2008, and it finally became part of the lexicon. DJ Patil and Jeff Hammerbacher of LinkedIn and Facebook are credited for popularizing the term.
2009: Johan Oskarsson revived the term NoSQL (a variation that had been used since 1998) in 2009, when he hosted a conversation on "open-source, non-relational databases."
2013: IBM released statistics suggesting that 90% of the world's data had been created in the previous two years.
2015: Google Voice's speech recognition, which uses Deep Learning algorithms, saw a 49 percent performance increase
2018: According to Bloomberg's Jack Clark, 2018 was a watershed moment for artificial intelligence (AI). Over the course of the year, Google's overall number of AI-related software projects grew from "sporadic use" to over 2,700.
Data Science has discreetly developed to cover enterprises and organizations all across the world in the last thirty years. Governments, geneticists, engineers, and even astronomers are now using it. Data Science's usage of big data evolved through time, and involved not only "scaling up" data, but also migrating to new technologies for processing data and changing how data is researched and evaluated.
That was a brief history of data science and the development of which had ushered in a new era in itself along with the technological revolution.