Along with the development of the field of data science, the term itself has also gained traction and evolved into a most talked about topic. There's no denying that data science's popularity in recent times has skyrocketed as data gathering evolved along with technology and massive data output. The world today has gone past the tedious and costly data programs and mainframes.
Data science became the popular field it is today, all thanks to the rise of programming languages like Python and techniques for collecting, analyzing, and interpreting data.
The merging of the established discipline of statistics with a very nascent one—computer science—is largely the narrative of how data scientists became fashionable. Only lately has the phrase "Data Science" been coined to describe a new profession tasked with making sense of massive amounts of data.
Making sense of data, on the other hand, has a long history and has been debated for years by scientists, statisticians, librarians, computer scientists, and others. The history below shows how the phrase "Data Science" has evolved over time, as well as attempts to define it and related terms.
As mentioned, Data science finds its foundation and beginning in Statistics. The advancement of Data science and its evolution has been majorly facilitated by the arrival of Artificial Intelligence, Machine learning, and the Internet of Things. Data science began to grow in other industries, including medicine, engineering, and more, as a result of the influx of fresh data and corporations seeking new ways to improve profit and make better judgments.
In this article, we'll give a comprehensive overview of data science and its development, from its humble origins as a statistician's dream to its current status as a distinct science acknowledged by every industry.
We may say that data science is the result of combining applied statistics and computer science. The resulting branch of research would make use of modern computing's incredible capabilities. Scientists found they could utilize data to address real-world problems and produce accurate fact-based forecasts in addition to collecting data and solving statistical difficulties.
1963: John W. Tukey, an American mathematician, initially expressed the data science dream in 1962. Nearly two decades before the first personal computers, he predicted the rise of a new field in his now-famous paper "The Future of Data Analysis." Tukey was not alone in his early understanding of what would become known as "data science," even though he was ahead of his time. Peter Naur, a Danish computer engineer, was another early pioneer, and his book Concise Survey of Computer Methods has one of the first definitions of data science which says "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 founded, with the mission of "linking traditional statistical methodology, the knowledge of domain experts and modern computer technology, to transform data into knowledge and information," putting "pre" data scientists like Tukey and Naur's theories and predictions into practice.
The 1980s & 1090s: With the inaugural Knowledge Discovery in Databases (KDD) workshop and the foundation of the International Federation of Classification Societies in the 1980s and 1990s, data science began to make considerable advancements (IFCS). These two organizations were among the first to concentrate on educating and training professionals in data science theory and technique (though that term had not yet been formally adopted).
At this point, data science began to attract the attention of top executives looking to profit from large data and applied statistics.
1994: The emerging phenomena of "Database Marketing" was covered in Business Week in 1994. It represented the process by which organizations gathered and analyzed massive volumes of data in order to understand more about their consumers, competitors, and advertising strategies. The only issue at the time was that these businesses were inundated with more data than they could handle. The first wave of interest in establishing separate positions for data management was sparked by massive volumes of data. Businesses appeared to require a new type of employee to make data work in their favor.
The 1990s and early 2000s: Data science has definitely evolved as a recognized and specialized field in the 1990s and early 2000s. Several data science academic publications began to circulate, and proponents such as Jeff Wu and William S. Cleveland continued to help expand and expound on data science's usefulness and potential. In fact, William S Cleveland is known to have brought about the modern data science conception.
2000s: Technology made huge strides by making internet connectivity, communication, and (of course) data collection practically widespread. William S. Cleveland outlined proposals for training data scientists to address future needs in 2001. He offered Data Science: An Action Plan for Expanding the Technical Areas of Statistics as an action plan. It outlined methods to improve data analysts' technical skills and breadth of knowledge, as well as six areas of study for university departments. It advocated for the creation of specific research resources in each of the six categories. His idea extends to government and industry research as well. Software-as-a-Service (SaaS) was founded in 2001. This was the forerunner to the use of cloud-based software.
2005: Big data makes its debut in 2005. With tech behemoths like Google and Facebook amassing massive volumes of data, new data-processing methods were required. Hadoop stepped up to the plate, and Spark and Cassandra followed suit.
2014: As data became more important and organizations became more interested in detecting patterns and making better business decisions, demand for data scientists grew dramatically in various parts of the world.
2015: Artificial Intelligence (AI), Machine Learning, and Deep learning all make their debut in the field of data science in 2015. Over the last decade, these technologies have generated advancements ranging from personalized shopping and entertainment to self-driving vehicles, as well as all the insights needed to effectively bring these real-world AI applications into our daily lives.
2018: One of the most significant aspects of the evolution of data science is the introduction of new regulations in the field.
2020s: We're seeing further advancements in AI and machine learning, as well as an ever-increasing demand for qualified Big Data specialists.
So, who invented data science?
While William S. Cleveland is credited with establishing data science as a separate discipline in the modern era. The term "data science" dates back to 1974, when Peter Naur proposed it as a replacement for the term "computer science." Hence the word data science was invented by Peter Naur.
DJ Patil and Jeff Hammerbacher are credited with coining the term "data scientist" in 2008. Though the National Science Board used the term in their 2005 study "Long-Lived Digital Data Collections: Enabling Research and Education in the Twenty-First Century," it refers to any significant function in administering a digital data collection.
We might properly question, "Where do we go from here?" given how much of our world is currently fueled by data and data science. What does data science's future hold? While it's tough to predict exactly what the future's breakthroughs will be, all signals point to machine learning's crucial role. Data scientists are looking for new ways to leverage machine learning to create AI that is more intelligent and self-aware.
To put it another way, data scientists are working nonstop to improve deep learning and make computers smarter. These advancements could lead to advanced robotics combined with a formidable AI. Experts believe that AI will be able to understand and interact with humans, self-driving cars, and automated public transportation in a world that has never been more connected. Data science will help to create this new world.
On the plus side, we might be witnessing the dawn of a new era of massive labor automation in the not-too-distant future. The healthcare, finance, transportation, and defense industries are projected to be transformed as a result of this.
Many lessons may be learned from history, and data science is no exception. Here's what we can learn from data science history:
There was a period when data wasn't as available as it is now, and individuals weren't as willing to openly exchange it. This isn't to say that privacy and other ethical considerations aren't still present, and data scientists must be able to work within an ethical framework as the data tsunami expands. Even while data is more accessible, much of it is still unstructured, allowing for novel analysis methods.
Big data necessitates big analysis, and as technology advances, data scientists' high-performance computing skills must grow as well. This includes the ability to perform data mining and predictive analytics on large amounts of data.
Unlike in the past, when data scientists predominantly worked in the information technology industry, today's data scientists operate in a wide range of industries, assisting firms in making data-driven decisions that alter how they compete in the marketplace. Data scientists must be well-versed in data communication and strategic decision-making to be successful.
As industrial demands alter, data science will definitely adapt. One thing is certain: data scientists will always be in high demand. As long as data exists, highly skilled professionals must be able to interpret it.
The data science renaissance is still in its early stages, and there's never been a greater thing to become involved in. Data science is an intriguing and rapidly growing field that is becoming increasingly important. As a result, there is a huge demand for qualified workers.
The tremendous demand for data scientists, along with a scarcity of qualified professionals, has created a once-in-a-lifetime opportunity for eager students. And, as data science applications become more widely adopted across industries and organizations, demand will continue to rise.
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