A realistic time travel into the future of data science

Introduction

Data science is a subject that uses statistical operations, mathematical techniques, and knowledge of various other subjects like machine learning to transform data. With the help of machine learning models, it becomes easy to understand the underlying patterns in data. We can then use different types of optimization techniques to bring unstructured data sets into a format that can be used for business requirements. In simple terms, data science holds the key to unlocking the hidden potential of data and utilizing it for driving the growth of a business.

Deriving meaningful insights out of large data sets is a job easier said than done. It not only requires precision and expertise but also requires knowledge of data science tools and methodologies. A data science certification online can also be considered a prerequisite for a career in data science. In addition to this, communication skills are also necessary for the domain of data science as complex findings need to be communicated to the clients in a lucid and effective way.

A realistic time travel into the future of data science

Scope of data science

The scope of data science is very vast and covers technologies like artificial intelligence, machine learning, data mining, business management, statistics, probability, and business intelligence as well.

• Apart from these core areas, the scope of data science is being supplemented by new areas like data integration. Data integration allows the consolidation of data on a single platform so that it can be analyzed with a lot of ease.
• The second important technology under the scope of data science is called distributed architecture. Although machine learning is already under the scope of data science, we are now looking at automating the process of machine learning, and the technology is known by the name auto ML. The aim is to move towards less code or codeless machine learning technology in which large data sets can be used to train machines on their own.
• Data visualization is also an important technology under the scope of data science that involves the easiest possible interpretation of complex information that has been derived after data mining and data analytics. It is in this context that tableau, dashboards, and business intelligence are overlapping and coordinating technologies that can help in enhancing the scope of data science.
• The various processes related to data extraction, processing, analytics, and management have been given the name of data engineering. Data engineering also helps in effective decision-making as it supplies organizations with helpful and quantitative information. In simple terms, this is the area of decision science that comes under the scope of data science itself.

Where is data science heading?

Data science is not a static subject but its scope and horizons are increasing in scope and are much more dynamic than we can imagine. Data-based processes and operations will only increase in the time to come. It is in this context that the scope of data science will increase further and critical business processes including complex business challenges would require inputs from data science. So, we conclude that data science is heading towards a future where its need would be inevitable in different applications and sectors.

The challenging area is the shortage of data scientists that we may face in the time to come. There is already a mismatch between the data science skills needed in the industry as well as those provided in the academic sector. In order to resolve this mismatch, it is extremely important to initiate internships and skill development programs.

Data science trends and developments in various sectors

Let’s briefly explain the trends in the domain of data science that we would witness in the coming years. A report by IBM noted that there would be an increase in data science jobs by more than 40% in the next 3 years. In the Indian context, as many as 3 lakh to 4 lakh jobs would be created in various sectors related to data science. The lucrative prospects of data science jobs would also increase as companies would become much more dependent on data than ever before.

There would also be a lot of progress in the research and development related to data science. Different kinds of advanced algorithms would be deployed to make sense of data. Machine learning algorithms would also be used to derive critical insights from large data sets. These data sets would also drive the process of decision-making and predictive analytics. In simple terms, data science would channel the growth of various businesses in the time to come.

When it comes to the academic sector, we may witness a paradigm shift in teaching and learning methodologies as well as a transition from theoretical to practical learning. It is highly possible that the subject of data science would slowly make its way as an essential component of the academic sector.

The relative subjects pertaining to data science like data mining, data processing, data analytics, and data management would help in bridging the gap between the academic and the industrial sector. In the present time, the academic sector would also lay prime focus on data visualization techniques because this would help in improving the data literacy of the business sector as complex findings would be expressed in a simplified manner.

An insight into the future of data science

The future of data science would be driven by technologies like artificial intelligence, machine learning, and deep learning. With the help of data science,  we would be able to carry out personalization, predictive analytics, customer analytics, and product recommendation. These applications hold huge potential when it comes to sectors like e-commerce. In addition to this, data science would also help in providing accurate search engine results in the future with the help of advanced analytical algorithms.

The role of machine learning in search engine optimization would be very crucial in the time to come. We would also experiment with code-free environments with the help of machine learning tools and techniques. This will have the advantage of taking the science of data very close to the common man. The computational power of virtual machines would also increase by a significant amount.

This would be possible with the help of three advanced computational technologies including cloud computing, edge computing, and quantum computing. Needless to mention, quantum computing would boost the computational power of machines and help in deriving time-bound solutions to very complex problems.

The way ahead

The future of data science is very bright and this has ramifications for various sectors. The exponential increase in applications of data science connotes that the progress of data science would be unstoppable in the coming times. What we have achieved in decades with the help of other technologies would be achieved in years with help of data science.

Conclusion

The need of the hour is to provide training in data science at an early stage and also prioritize data science courses so that the transition of students into the industrial domain happens in a hassle-free manner. Meanwhile, it is extremely important to lay down a concrete policy framework for data regulation so that the upcoming age of data can prove beneficial to all the stakeholders without any further hurdles.