Basically, data science involves the use of machine learning tools, alongside the application of analytics to unlock value in data. Currently, there is a wave of growth sweeping through the field of data science as a result of the increase in amount of data, advanced machine learning algorithms, and better computing resources.
Due to these indispensable features and effects that data science has and will continue to have on our lives, this article will expound on the need for data science, the trends leading to the future of data science(1), and how companies and individuals can adequately prepare for the future.
The Future of Data Science
Having elaborated on the concept of data science, it is pertinent to consider certain factors that demonstrate the great potentiality evident in the future of data science. These factors explain the reasons why contemporary businesses and organizations will and have begun to look to the positive future data science holds for them.
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Companies’ Inability to Handle Data
Every minute, different businesses and organizations constantly gather data for their respective transactions. However, the problem is that most of these organizations share a common challenge; which is analyzing and categorizing the data that has been gathered and stored.
Thus, in such dire situations, the only solution for the companies is the service of a data scientist. With properly executed data science, these organizations will experience increase in productivity through adequate and professional handling of data.
Indeed, the future of data science will bring a solution to companies’ inability to effectively handle data.
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Revised Data Privacy Regulations
The reality is that more and more people are exercising increasing caution and alertness when it comes to sharing their data with businesses. A large percentage of individuals are skeptical about giving up a certain degree of control to companies. This is simply as a result of the increase in the awareness of data theft and its negative effects.
Thus, reputable companies are sensitive and deliberate in keeping their clients’ information safe and intact. To buttress this, the GDPR – General Data Protection Regulation, was passed by states of the European Union in May 2018.
It has also been reported that such regulation for data protection shall again be passed by California in 2020. Hence, with the recent Revised Data Privacy Regulations taking place, the future of data science is very bright.
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Data Science is Constantly Evolving
As it is, change is the only constant thing in life. Therefore, any field with no potential for development is at the risk of extinction. Delightfully, data science is evolving and undergoing progressive changes that guarantee a plethora of opportunities in the nearest future. In no time, the job specifications in data science would require specific specializations.
Thus, individuals who decide to pursue a career in data science can maximize their opportunities through these specific specializations. Indeed, the data science community is rapidly evolving; the train is moving, and many are getting on-board.
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An Astonishing Incline in Data Growth
Do you know that you generate a certain amount of data on a daily basis? Yes, everyone consciously or subconsciously does. And as time passes, the amount of data we generate everyday will only be on the rise. It has been claimed that the amount of data currently available today will sporadically multiply at the speed of lightning.
Consequently, it is obvious that with increase in data, there will equally be a high demand for data scientists so as to manage the existing data sets and structures. The balance and management of this data equilibrium greatly depends on data science future.
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Virtual Reality Will Be Friendly
Without doubts, all over the world there is an upsurge in the contributions of artificial intelligence, and many businesses are depending on it. With the introduction of modernized and advanced concepts such as Neutral Networking and Deep Learning, big data prospects are sure to flourish with these current innovations.
In almost every ramification of life, machine learning is currently being introduced and used. In addition, VR – Virtual Reality and AR – Augmented Reality, are passing through great developmental processes. Furthermore, there is a high chance that the interactions and interdependency between humans and machine, is heading towards monumental increase and progress.
Thus, in the nearest future, Virtual Reality and other related concepts will be largely very friendly.
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Blockchain Updating with Data science
Blockchain refers to the major technology that deals with cryptocurrencies such as Bitcoin. For the data transactions within the Blockchain exchange to be secure and recorded, there is a need for data science. With data security, there will be a growth within the industry. Data scientists will be charged with maintaining the data and resolving every data-related issue.
Also Read: What is Data Science? Everything You Need to Know
How to Prepare for the Future of Data Science
Having understood that there are great potentials in the future of data science, you’re probably wondering: how can my company prepare? We have highlighted below four major ways to maximize the chance of excelling in a highly digitalized world with the rise of data science:
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A Data Science Unit
It is important to know that if a business or organization has a particular size; then the creation of a dedicated data science unit is the best decision to make. The benefit of creating an analytics unit is that it makes it a lot easier to reuse employees’ skills.
Any industry or company can create and optimize the existence of a data science unit; from banking and finance, insurance, academia, government agencies to business corporations also.
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Standardization
The practice of standardized procedures is equally necessary. The advantage of doing this is that it makes the digitalization and probably automation of the procedures very easy in the nearest future. Therefore, the data gathered from easier to scale automated processes are usually less complicated and less susceptible to error than manually gathered procedures.
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Adoption of Data Science
As the world is getting more advanced, it is necessary for companies to adopt the practice of using machine learning algorithms and using these outputs in making company decisions. However, the issue here is that most employees would see this step as a nullification upon their relevance in the company.
Therefore, it is paramount that the employees combine their existing skills with the algorithms, in order to produce even higher tactical company decisions. It should be noted that the future of work depends on the success of human and machine cooperation.
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Always Experiment
Experimentation has always been important in any field. Thus, it is necessary to explore new datasets and test how they can be modified to optimize your existing models. The fact is that there is a limitless chain of unexplored data waiting to be utilized. The point is that, regardless of a chance of failing when experimenting, never be afraid to attempt newer explorations of datasets. In the end, you’d be glad you kept on trying.
Conclusion
It is high time the entirety of humans ventured into the untapped potentials in data science. As the amount of data constantly increases, it becomes inevitable for us to adapt and maximize the opportunities in the future of data science.
