Data science has grown and changed over time. Statistics was the start of data science. Machine learning, artificial intelligence, and the internet of things are all ideas that have been added to data science as it has grown. With so much new information coming in and businesses looking for ways to make more money and make better decisions, data science started to spread to other fields, like medicine, engineering, and more.
In this article, we’ll summarize how data science has changed over time, from its humble beginnings as a statistician’s dream to its current state as a unique science recognized by industries you can think of.
Beginnings, Origins, and Predictions
We could say that the idea of combining applied statistics and computer science led to the creation of data science. The new field of study would take advantage of how powerful computers are today. Scientists figured out that they could not only collect data and solve statistical problems with it but also use it to solve problems in the real world and make accurate predictions based on facts.
John W. Tukey, an American mathematician, came up with the idea of data science for the first time in 1962. In his now-famous article “The Future of Data Analysis,” he predicted that a new field would have to be created almost 20 years before the first personal computers.
In 1977, the IASC (International Association for Statistical Computing) came into existence. Its claimed goal was to “transform data into information and knowledge by integrating traditional statistical methods, state-of-the-art computer technology, and the insight of subject-matter experts.” This gave credence to the ideas of “pre-data scientists” like Tukey and Naur.
The 1980s and 1990s: The first Knowledge Discovery in Databases (KDD) workshop and the start of the International Federation of Classification Societies were big steps forward for data science (IFCS). These two organizations were among the first to focus on teaching professionals about the theory and practice of data science (though that term had not yet been formally adopted).
At this point, top professionals who wanted to make money from big data and applied statistics started paying more attention to data science.
In 1994, BusinessWeek wrote about a new trend called “Database Marketing.” It talked about how businesses gathered and used huge amounts of data to learn more about their customers, their competitors, or how to advertise.
From the 1990s to the early 2000s, it’s clear that data science has become a well-known and specialized field. Several academic journals about data science began to be published, and proponents of data science like Jeff Wu and William S. Cleveland continued to help develop and explain why and how data science is important.
In the 2000s, technology made huge steps forward by making the internet, communication, and (of course) data collection available to almost everyone.
In 2005, big data comes into play. Since tech giants like Google and Facebook were finding a lot of data, new technologies that could handle it were needed. Hadoop was up to the task, and Spark and Cassandra came out later to help.
In 2014, demand for data scientists started to grow rapidly in many parts of the world. This was because data was becoming more important, and organizations wanted to find patterns and make better business decisions.
2015 marks the official start of machine learning, deep learning, and Artificial Intelligence (AI) as parts of data science. In the last ten years, these technologies have led to a lot of new ideas, like personalized shopping and entertainment, self-driving cars, and all the knowledge we need to use these real-world AI applications in our everyday lives.
In 2018, new rules in the field may be one of the most important parts of how data science has changed.
In the 2020s, AI and machine learning are making more progress, and there is a growing need for qualified professionals in Big Data.
What’s In the future for Data Science?
Given how much of our world runs on data and data science right now, it’s a fair question to ask, “Where do we go from here?” What are future prospects for data science? Even though it’s hard to say what the most important future innovations will be, machine learning seems to be the key to everything. Data scientists are looking for ways to use machine learning to make AIs that are smarter and more self-sufficient.
In other words, data scientists work hard to make deep learning better so that computers can be smarter. These changes could lead to advanced robots that work well with a strong AI. Experts think that AI will be able to understand and communicate with people, self-driving cars, and automated public transportation in a more connected world than ever. Data science will make this new world possible.
On the bright side, we might soon live in a time when a lot of work is done by machines. People think this will change the way healthcare, finance, transportation, and defense work.