Data science becomes the most demanded academic choice

Data science has recently become one of the most popular academic choices among young people and working professionals, as well as one of the most recommended courses by career counseling services. Various academic journals began to recognize data science as an emerging discipline throughout the 2000s. The National Science Board advocated for a data science career path in 2005 to ensure that there would be experts capable of managing digital data collection and analysis.

By this point, a new industrial context had pushed companies to view data as a commodity on which they could profit. “Instead of competing on traditional factors, companies are beginning to employ statistical and quantitative analysis and predictive modeling as primary elements of competition,” according to experts. “If you don’t know what to do with the data, data scientists use it in its raw, unstructured, and complex format.” The emphasis is on actionable data, which combines big data and business processes to assist you in making the best decisions. 

This prompted universities to create a brilliant curriculum to address the industrial need for skilled manpower.

Simply Investing in costly data software will yield no results unless the data is analyzed by experts to yield actionable insights. These insights assist you in understanding the organization’s current position, market trends, challenges and opportunities, and so on. Actionable data enables you to make better decisions and do what is best for the business.

Nonetheless, Google Chief Economist Hal Varian told the McKinsey Quarterly in 2009 that he was concerned about a lack of qualified individuals to analyze the “free and ubiquitous data” being generated. “The complementary scarce factor is the ability to understand that data and extract value from it,” he explained. I believe those skills—being able to access, understand, and communicate the insights gained from data analysis—will be extremely valuable.” Data science has evolved and permeated nearly every industry that generates or relies on data over the last six years.

Kenneth Cukier writes in The Economist in 2010 that “Data scientists combine the skills of a software programmer, statistician, and storyteller/artist to extract the nuggets of gold hidden beneath mountains of data.” The technology industry’s multidimensional requirements created a huge demand for such experts, which is the context for launching the new discipline of data science.

Data is already being generated in abundance. The question now is how to manage these massive amounts of data. The issue is collecting, tagging, cleaning, structuring, formatting, and analyzing this massive volume of data in one place. How to collect the data? Where will it be stored and processed? How should we share our findings with others? A scientific answer is required. Cleaning data, training models, predicting results and insights, interpreting the results, and many other data science processes can be automated using automated machine learning. These tasks are typically performed by data science teams.

The Internet of Things (IoT) has trillions of connections and is a network of physical things embedded with software, sensors, and cutting-edge technology. This enables different devices on the network to communicate with one another and exchange data over the internet. By combining the Internet of Things with machine learning and data analytics, you can increase the system’s flexibility and improve the accuracy of the machine learning algorithm’s responses.

While many large-scale enterprises are already implementing IoT in their operations, SMEs are beginning to follow suit and become more data-savvy. Demand is increasing as a result of these new trends. When this occurs in full swing, it is unavoidable that traditional business systems will be disrupted, resulting in massive changes in how business systems and processes are developed and used. The demand for skilled experts to manage these demands is enormous. The use of public and private cloud services for big data and data analytics will be one of the major data management trends in 2022.

The most recent trend in data science models and artificial intelligence can help. However, data storage remains a concern. Around 45% of enterprises have moved their big data to cloud platforms, according to research. Cloud services are increasingly being used by businesses for data storage, processing, and distribution.

Data scientists are now indispensable to any company for which they work, and employers are willing to pay top dollar to hire them. In addition, degree programs in data science have emerged to train the next generation of data scientists.

Although data science is not a new field, it has evolved significantly over the last 50 years. A journey through data science history reveals a long and winding path that began in 1962 when mathematician John W. Tukey predicted the effect of modern-day electronic computing on data analysis as an empirical science. However, many data scientists now believe that Python is and will remain an essential part of data science. The majority of the curricula included Python in their curricula. To manage the infinite possibilities of data processing, the world must produce a large number of experts in the near future.