The Evolution of Data Science | Data Science Training | intellipaat

1963: The data science dream was first articulated in 1962 by American mathematician John W. Tukey. In his now-famous study "The Future of Data Analysis," he foresaw the emergence of a new field about two decades before the first personal computers. Tukey was ahead of his time, but he wasn't the only one who had a basic understanding of what would later be called "data science." Another early pioneer in the field of data science was the Danish computer engineer Peter Naur, whose book Concise Survey of Computer Methods contains one of the first definitions of the term: "The establishment of the data science field is further followed by the data relationship and its representation assigned to other sciences and fields.

1977: The International Association for Statistical Computing (IASC) was established in 1977 with the goal of putting "pre" data scientists like Tukey and Naur's theories and predictions into practice by "linking traditional statistical methodology, the knowledge of domain experts, and modern computer technology."

The 1980s & 1090s: With the introduction of the Knowledge Discovery in Databases (KDD) workshop and the founding of the International Federation of Classification Societies, data science achieved considerable advancements in the 1980s and 1990s (IFCS). These two organizations were among the first to put a priority on educating and training professionals in the theory and technique of data science (though that term had not yet been formally adopted). At this point, top executives began to focus on data science because they intended to profit from large data and applied statistics.

1994: Business Week in 1994 included a story on the "Database Marketing" phenomenon when it was just beginning. It represented the method through which businesses gathered and examined enormous amounts of data in order to learn more about their target audiences, rivals, and advertising tactics. The only problem at the time was that these companies were overrun with data and couldn't cope. Massive amounts of data generated the initial wave of interest in creating specialized professions for data management. To use data to their advantage, businesses seemed to need a new kind of employee.

The 1990s and early 2000s: In the 1990s and the early 2000s, data science unquestionably developed into a recognised and specialized discipline. Academic articles on data science started to appear, and proponents like Jeff Wu and William S. Cleveland kept explaining the value and promise of the field. In reality, William S. Cleveland is credited with creating the idea of modern data science.

2000s: By effectively enabling internet connectivity, communication, and (of course) data collecting, technology made enormous strides. In 2001, William S. Cleveland proposed ideas for educating data scientists to meet future needs. He proposed the following action plan: Data Science: An Action Plan for Increasing the Technical Aspects of Statistics. It provided guidance on how to advance the technical proficiency and depth of knowledge of data analysts as well as six areas of study for academic divisions. In each of the six categories, it promoted the development of specialized research resources. His proposition encompasses both academic and commercial research. SaaS, or software as a service, was established in 2001. This served as the model for using cloud-based software.

2005: The year that big data first appears. New data-processing techniques were needed as a result of the large amounts of data that tech giants like Google and Facebook were collecting. Spark and Cassandra then took Hadoop's lead and stepped up. 

2014: The need for data scientists increased significantly around the world as data's significance and enterprises' interest in finding patterns and improving business decisions increased.

2015: In the discipline of data science, Artificial Intelligence (AI), Machine Learning, and Deep Learning all make their debuts. These technologies have produced improvements over the past ten years in everything from tailored entertainment and shopping to self-driving cars, as well as all the knowledge required to successfully integrate these real-world AI applications into our daily lives. 

2018: The implementation of new rules in the sector is one of the most significant features of the development of data science. 

2020: AI and machine learning continue to advance, and there is a rising need for experienced Big Data experts.