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How Data Science Evolved Since Its Beginning

If you are looking forward to getting into an institute of Data Science, then you might want to know the evolution of Data Science along with its growth and span in general.

We might take the word Evolution into consideration when we describe the progression of Data Scientists. In this scenario, Evolution refers to the changes that took place in methods, technologies, and systems that pertain to data science and the work of Data Scientists. 

The usage of statistics and statistical models are prevalent within the arena of Data Science. It began with statistics and has continued to evolve ever since.  In its journey, it has started to incorporate practices and concepts such as Machine Learning, Artificial Intelligence, and others. The more data became available, the more businesses, industries, and organisations started to accumulate and store it in large capacities. With the rapid growth of the Internet, the IoT, and available data volumes, there has been an overflow of new information which is commonly known as Big Data. Ever since, businesses opened their doors for more profits and improved decision-making drive, the use of Big Data began to span over other fields, such as medicine, social sciences, and engineering.

Departure from statistics

As opposed to a general statistician, a Data Scientist has a substantial understanding of software architecture. They are equipped with the knowledge of multiple programming languages as well. The job of a Data Scientist is to first define the problem, then identify the prime sources of information. After that, they can design a framework for the collection and the screening of the required data. Software is primarily responsible for data collection, data processing, and data modelling. A Data Scientist uses the laws of Data Science, and the interconnected practices as well as other sub-sectors of the latter to obtain a deeper insight into the data assets that are kept for review. 

The gradual growth

Numerous timelines and dates can be used to trace the gradual growth of Data Science along with its present impact on the Data Management Industry. 

Some of the prominent instances are provided below to trace the foundation of Data Science:

In 1962, John Tukey wrote about a new movement in the domain of statistics, He said, “…. as I’ve watched mathematical statistics evolve, I’ve had the cause to wonder and to doubt…. I’ve come to feel that my central interest is in data analysis….” Here, John Tukey is indicating the amalgamation of statistics and computers, at a time when, within just a few hours, statistical results could be presented, instead of days or even weeks had it been done manually. 

In 1999, Jacob Zahavi came up with the proposal for new tools that needed to be used for handling huge amounts of information available to Data mining businesses and industries. 

In 2015, Jack Clark wrote that the said year had been extremely significant for Artificial Intelligence. In Google, there was a “sporadic upsurge” in projects that were using Artificial Intelligence. 

Countless examples can tell the story of Data Science and how it came into being an essential part of almost every industry and organisation. 

So, now let us see how Data Science influences those organisations. 

In the last ten to fifteen years, Data Science has grown to be an indispensable part of businesses all over the world. From governments, health sectors, agricultural industries, to astronauts, everybody needs Data Science. During the evolution of Data Science, its use of Big Data was not just ‘scaling up’, it involved shifting to new and different systems for data processing and data analysis.

Data Science is now massively used in both business and academic research. Being a part of these involves robotics, machine translation, speech recognition, digital economy, and search engines. Data Science has broadened to incorporate health care, medical information, humanities and other fields of study in terms of research areas.

A revolutionary outcome of Data Science has been a gradual move to writing new conservative programming. It has also been discovered that Data Scientists were putting way too much of their effort into developing unreasonably complex algorithms when the easier ones worked more efficiently. As a consequence, innovative transformations began to cease. Many Data Scientists now think that extensive revisions might prove too unsafe, therefore, they attempt to break ideas into smaller sections where each of the parts gets examined, and then, carefully placed into the main data flow. 

Although this new point of view might save organisations time and money including the avoidance of some serious mistakes, they are still at risk for focusing on smaller constraints and avoiding the pursuit of bigger inventions.

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