Introduction 

The landscape of data science is continuously undergoing rapid change due to the emergence of new technologies in this domain. The amount of data that is generated in the present time is simply immeasurable. The cutting-edge research in the domain of data science is allowing us to cope with large volumes of data. The entire process of data science is aimed at collecting data, processing it, and analyzing it to derive important information from it which can then be used for critical decision-making. According to a report by Salesforce, new technological advances would put the demand for data scientists at its highest level, and professionals with data science skills would command higher positions in various organizations. 

The importance of data science is not a hidden fact. Recognizing the demand for this subject, data science training in Noida, Delhi, Bangalore and other metropolitan cities of the country has already started. The progress in data science can be broadly gauged at three different levels. 

• The first level is the educational field of data science which is getting momentum with the help of data science courses. 

• The second field is related to skill development in which data science skills are leveraged by organizations to power different processes and platforms. 

• The third field is related to cutting-edge research in data science that is taking place at the scientific level. 

Let us now understand the technologies that are continuously influencing the growth of data science.

The technology of digital twins 

The technology of digital twins is a concept that replicates physical items and creates their existence in the digital world. In simple terms, this technology is much more about simulation and digital representation of physical objects. 

There are two important aims of this technology. 

• The first aim is to explore the advantages and disadvantages of a particular device, technology, or process in the digital world before it can be completely replicated in the physical world. This not only saves critical infrastructure from being created but also allows a pre-assessment test regarding the feasibility of a setup. 

• The second important advantage of digital twins technology is that it allows us to examine different functionalities of a product or service in the digital domain itself. This is important because a feedback mechanism can be created to take a product from its prototype to the finished stage in the digital domain itself. This benefits the overall process of product development and allows the launch of a finished product in the market without the need to move to the pilot stage first. 

It is highly likely that the upcoming products, services, and technologies would greatly benefit from digital twins in the times to come.

The quantum revolution 

Quantum computing is going to change the way extreme computations are performed by computers. Quantum computing is not only about complex computation but is also about the power of machines to perform those types of calculations which would otherwise require hundreds of years to complete. 

In simple terms, Quantum computing is about the usage of qubits that take the values of both zero and one and this is what gives high computational power to machines. Although the stage of quantum computing is still in its infancy, it is expected that this process will gain traction in the coming times.

Since the amount of data is growing by leaps and bounds, it is highly likely that we might need the power of extreme machines as well as the aid of quantum computing to solve complex computational problems. It is in this context that Quantum computing provides a direct remedy to the challenges that unstructured data sets are throwing at us.

Auto ML 

Auto ML is becoming increasingly important in the present time because it is holistically used in various data science processes. The prime benefit of automated machine learning is that it drastically improves the accuracy of machine learning algorithms in predicting a particular result or solving a particular problem. It also increases the efficiency of machines in executing a particular task and deriving solutions to complex problems.

A Gartner report predicts that almost 40% of the machine learning processes would be automated in the next three years. Automated machine learning will greatly improve the prospects of research and development that is currently going on in the domain of data science.

The bottom line 

In addition to the above-mentioned technologies, data science is greatly benefiting from cloud services which are not only providing unlimited storage options but also great processing power. A new range of technologies is developing as our digital footprint is increasing and a new technological revolution is unleashing.