For a discipline only 50 odd years old, data science has managed to permeate an expansive array of industries. The modern world is inundated with data. Without scientific processes and algorithms to harness the power of the data, crucial insights would be lost. Data science helps us find actionable insights that have extensive applications. The interdisciplinary field of data science encompasses data mining, data analysis, machine learning, computer programming, etc.
In this article, we explore how the practice of data science began, how it became so pivotal to the internet of things, and what makes it an integral part of machine learning and beyond. The wide implications of progress in data science mean that it’s hard to find an industry that isn’t a beneficiary. From the health sector to law enforcement and naturally information technology, data science has changed the way things are done wherever large-scale statistics are concerned.
If data science is where you intend to make a mark for yourself, there’s no better time to start than now. But first, we need to get you primed for the next data science Bootcamp on your calendar. We will take you through the groundbreaking journey data science has had over the years and the way it has evolved to be ubiquitous. Let’s get right into it.
The Birth of Data Science
The collection and analysis of data have been a part of human civilization for hundreds of years. It began with simple statistics & over the years it has become sophisticated to accumulate and analyze enough data to make modern technology possible. In the age of information explosion, data science helps us extract insights in gigantic data sets. An absence of cogent data science techniques renders big data meaningless.
It was a long journey from statistics to data analysis and finally the specialized discipline of data science. We attempt to track its magnificent journey through the years.
1962 – John Tukey’s call to action with his pioneering paper ‘The Future of Data Analysis’ awakened the statistical fraternity to the potential of data. Later, in 1977 Tukey solidified his contribution to the fledgling field of data by writing Exploratory Data Analysis that emphasized the importance of data usage to test hypotheses.
1974 – Another pioneering figurePeter Naur published Concise Survey of Computer Methods where notably the term data science is used for the first time. Naur defines data science as, “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”
The theories Tukey and Naur gave led to the subsequent corroboration of their concepts by the formation of the International Association for Statistical Computing (IASC) in 1977.
1994 – Bloomberg BusinessWeek published an insightful article about Database Marketing. It’s interesting to note that they recognized the lack of a scientific method to make sense of large data files. Perhaps, it was from this crisis of crunching numbers to no avail that the need for data science arose.
1996 – The International Federation of Classification Societies (IFCS) convened the first conference with explicit mention of data science called, “Data Science, Classification & Related Methods”. The Classification Societies were known to use terms like data mining, data analysis, and data science in their articles.
1997 – Data Mining and Knowledge Discovery were launched giving a solid base to data mining as a practice on its own. The paper From Data Mining to Knowledge Discovery in Databases written by Fayyad, Piatetsky-Shapiro & Smyth served as an inspiration to this change.
2001 – When William S. Cleveland published Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics, he proposed a shift from statistics to the technical field of data science. He wanted to expand the field of data science into 6 technical areas and distribute university resources accordingly.
2001 – Leo Breiman wrote Statistical Modeling: The Two Cultures, in which he suggested a switch to the more practical and scalable Algorithmic modeling from the more traditional data models.
2002 & 2003 – The launches of Data Science Journal in 2002 & Journal of Data Science in 2003 solidified the position of data science as a means of data analysis and computing.
A New Era Begins
Big data made headways towards the early 2000s and changed the way we perceived data forever. With the start of Google & Facebook, tech giants collected unprecedented amounts of data from their users. The 2000s saw major groundbreaking publications on data science that changed the face of data analysis. There was a rise in the number of data science proponents, from Hal Varian, the chief economist at Google at the time to DJ Patil contributed to popularizing and expanding the “new science”.
2009 – Researchers from the Research Center for Dataology and Data Science wrote the paper Introduction to Dataology and Data Science, in which they established data science as a distinct discipline from established sciences, as a new field of science.
2012 – Tom Davenport & D.J. Patil published the article Data Scientist: The Sexiest Job of the 21st Century and since then there has been no looking back.
2018 – The EU passed the General Data Protection Regulation act, expected to curb the exploitation of consumer data and protect their privacy.
2020s – Increasing relevance of data meant that organizations had to level up their processing abilities. This led to the emergence of database frameworks, newer programming languages, machine learning, and a host of other new technologies. Data Science has made a very real impact on the way Artificial Intelligence has become a part of the modern routine. Given the amount of public data out there, online and data privacy becomes a very real concern.
What The Future Holds
Needless to say, the internet has grown beyond our wildest imagination. From online shopping, gaming, social media to virtual reality, we as consumers have become intertwined with the consumption and insemination of data in countless ways. Artificial Intelligence has already made repetitive and predictable tasks easy for us.
Data Scientists are contributing immensely to deep learning making AI smarter and leading towards a more automated world. It has the potential to make our world ever more interconnected, safe and revolutionize medicine, transport, and production industries.
All of this progress has occurred in the infancy of data science, as this field grows exponentially, data scientists stand to become one of the most important professionals and facilitators of a bright and advanced future.