The Evolution of Data Science

The Evolution of Data Science

In layman’s terms, data science simply refers to dealing with data that is both structured and unstructured. Everything that happens in data science is related to cleaning, preparation, and analysis of large quantities of data. Data science also primarily involves programming, mathematical and statistical calculations, problem-solving techniques and techniques to decode data in simpler ways, analyze it with different aspects, and its alignment. Data scientists are experts engaged in extracting insights and information from data available to them.

The field of data science in modern times is of substantial use. Marketing and analytics firms are engaged in these fields, which are often employed and funded for targeted advertising by large corporations. The whole scandal of Facebook, London-based data firm Cambridge Analytica, President Donald Trump and the 2016 United States election is a remarkable example where data science was heavily involved.

Data Science has evolved to great lengths in recent years. Technologies like Cloud and Automation continue to reward the whole field and have been part of a lot of discussions when it comes to data. Experts say that it will help elevate a range of organizations, like capturing, storing and processing data seamlessly and data scientists today continue to rely on Cloud because it has helped totally revolutionize processes and made systems more intelligent.

As far as automation is concerned, there are many areas and processes in the field of data science which require effort. One of the biggest examples of data science processes is algorithm selection and optimization. Now, this particular thing appears to be a typical core. But with automation taking over, this highly intensive and time-consuming task can be automated and resources can rather be utilized in different ways. Data professionals can add value to other areas of work which will allow the field to further advance.

However, one large concern on everyone’s minds is if automation in Data Science means there is no need for data scientists. Human error and time are the major areas of concern at hand. If we are looking to build smarter systems which can effectively work, we need to minimize errors, be as accurate as possible in our work and save time. That is something not possible when a human is involved and therefore, we turn to machines to carry out this type of work. Automated systems are on the rise and many people have hinted at them as a threat to mankind in professional infrastructure.

An expert in data science, Sergei Eremenko is a Ukrainian-Australian scientist who believes that the need for data scientists will not become obsolete any time soon. The scientist was the youngest Doctor of Engineering (29 years of age) after Ph.D. at 24. At the age of 31, he became one of the youngest professors, after publishing two books at 27 and 29. Sergei Eremenko specializes in the subjects of Computer Science, Applied Mathematics, Soliton Theory, Data Science, Machine Learning, Quantitative Finance, etc.

There are areas where machines will not be of any work. The intensive and time taking chores can be taken care of by machines but the human effort will always be there in multiple areas. The ability to innovate is one progressive aspect that data science requires and all eyes turn towards human beings to carry it out.

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