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Evolution of Data Science

What is Data Science?

Data Science started with statistics, and has evolved to include concepts/practices such as Artificial Intelligence, Machine Learning, and the Internet of Things, to name a few. Data science makes use of data mining, machine learning, Artificial Intelligence techniques.

As more and more data has become available, first by way of recorded shopping behaviors and trends, businesses have been collecting and storing it in ever greater amounts. With the growth of the Internet, the Internet of Things, and the exponential growth of data volumes available to enterprises, there has been a flood of new information or Big Data. Once the doors were opened by businesses seeking to increase profits and drive better decision making, the use of Big Data started being applied to other fields, such as medicine, engineering, and social sciences.

The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data.

What is the role of the Data Scientist?

The Data Scientist defines the problem, identifies the key sources of information, and designs the framework for collecting and screening the needed data. Software is typically responsible for collecting, processing, and modeling the data. They use the principles of Data Science, and all the related sub-fields and practices encompassed within Data Science, to gain deeper insight into the data assets under review.

Data scientists just try to get insights from massive amounts of data that can help companies make smarter business decisions. Data science uses a wide array of data-oriented technologies, including SQL, Python, R, and Hadoop, etc.

Today’s emerging technologies, such as AI, IoT, 5G, robotics, blockchain, and so on, rely heavily on data, and only those who will be able to operate with data and translate them into profitable products will guide the digital business of the next future.

History of Data Science

There are many different dates and timelines that can be used to trace the slow growth of Data Science and its current impact on the Data Management industry, some of the more significant ones are outlined below.

The main concentration was towards these following points :

  • Cost 
  • Security 
  • Convenience 

Pushing rewind on Data Science:

Although data science isn’t a new profession, it has evolved considerably over the last 50 years. A trip into the history of data science reveals a long and winding path that began as early as 1962 when mathematician John W. Tukey predicted the effect of modern-day electronic computing on data analysis as an empirical science.

By 1981, IBM had released its first personal computer. Apple wasn’t far behind, releasing the first personal computer with a graphical user interface in 1983. Throughout that decade, computing seemed to evolve at a much faster pace, giving companies the ability to collect data more easily. However, it would be nearly two decades before they would start to convert that data into information and knowledge.

The present evolution of Data Science :

The present day mindset would be described as, Lots of data, 

Little intelligence! 

A New Era of Data Science:

Throughout the 2000s, various academic journals began to recognize data science as an emerging discipline. 

In 2005, the National Science Board advocated for a data science career path to ensure that there would be experts who could successfully manage a digital data collection.

1.Don’t take the data for granted.

2. Think big. 

3. Know the context. 

What is the future of Data Science?

Data Science Continues to Grow.

Being a data scientist can do a lot more good than bad. Data Science has evolved so much in past years and continues to do the same. Data scientists are in continuous demand because as long as there is data around, there has to be an efficient way to organise this data and put it to use. It is not a shocker that Data Scientists are in demand, they recieve a good pay and altogether it’s quite endearing for people to do this job who have an OCD for organizing anything, may it be data and files or literally whatever! 

During the last years, we have become witnesses of many data-driven technological innovations, 5G lightning-fast Internet speed, machine learning, cloud computing, and the blockchain concept, with such a noteworthy list being far from exhaustive. The explosion of data along with growing technological abilities is just the beginning, and our life is becoming “smarter” with technology innovations that might soon be integrated into all aspects of human life.

Scope of a Data Scientist:

Data Science is in Demand. And so are data scientists. The good news is that in 2010, all of that started to change as data science began to take center stage against the backdrop of significant advancements in computing technology.

For example, Apple introduced the iPad in January 2010. In June of that same year, Apple released its iPhone4. Consumers began to embrace technology—particularly mobile technology—at lightning speed. In July, Amazon published a press release stating that for the first time ever, it had sold more Kindle books than hardcover books.

As long as data exists, there must be highly-skilled individuals who can analyze it. The questions for the future are, just how much data will be available, where will it come from, and what new analysis techniques will have emerged by then to give us even greater insights?

Conclusion :

The data science industry is massive. In trying to define it, it becomes more unrecognisable. What does it mean to exclude something? Currently, there is no consensus. Unfortunately, that’s how things work in the world. It’s possible that colleges will train Wisdom Oracles in another fifty years to take pedestrian data scientists and turn their work into something truly valuable.

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