A brief history of Data Science
Data Science has revolutionized several different aspects of our world. Let’s take a look then to when and where data science comes from.
- In 1962, John W. Tukey writes in “The Future of Data Analysis” — The first milestone in the history of data science is globally recognized for the bright American mathematician John Tukey. The influence of John Tukey in statistical terms is enormous, but the most famous coinage attributed to him is related to computer science. In fact, it should be mentioned that he was the first to introduce the term “bit” as a contraction of “binary digit”.
- In 1974, Peter Naur published the Concise Survey of Computer Methods, which surveyed data processing methods across a wide variety of applications. The term “data science” is become clearer, he put his own definition on it: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”
- In 1977, the International Association for Statistical Computing (IASC) was founded.
- In 1989, Gregory Piatetsky-Shapiro organizes and chair the first Knowledge Discovery in Databases (KDD) workshop.
- In 1994, BusinessWeek published a cover story on “Database Marketing”
- In 1996, in the occasion of the conference of International Federation of Classification Societies (IFCS), for the first time, the term “data science” is included in the title of the conference (“Data science, classification, and related methods”). In the same year, Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth publish “From Data Mining to Knowledge Discovery in Databases”.
- In 1997, during his inaugural lecture as the H. C. Carver Chair in Statistics at the University of Michigan, Jeff Wu called for statistics to be renamed “data science” and statisticians to be renamed “data scientists”.
Since the beginning of the 21st century, data stockpiles have expanded exponentially, largely thanks to advents in processing and storage that is both efficient and cost-effective at scale. The capability to collect, process, analyze and display data and information in “real-time”, give us an unprecedented opportunity to conduct a new form of knowledge discovery. To process this huge amount of data, Data Scientists need high performance also of a large portfolio of technologies to speed up tasks and data processing in a matter of seconds.
Disruptive technologies like artificial intelligence, machine learning and deep learning are nowadays available for Data Scientists thanks to powerful platforms available in Cloud.