Data science was named the ‘sexiest job of the 21st century’ by Harvard Business Review. It is undoubtedly an exciting field that is also showing great promise for the future.
However, data scientists are not new to the corporate scene; they have been around for quite some time. The factors that sparked their surge are the gigantic amounts of data that need to be handled by corporations, and the realization that effectively processed data is a treasure trove of vital insights to propel businesses in the right direction.
The importance of data was rightly highlighted by Tim Berners Lee, who said “Data is a Precious Thing and will Last Longer than the Systems themselves.” Currently, corporations are dealing with data like never before. The sheer volume and variety of it are demanding specialist expertise to deal with it. So, companies are betting higher on data scientists.
Data Science: Relevance & Importance
Data science is a field where data is exploited for meaningful insights. Statistics and software languages are used in coercion to dig out metrics that are useful in business planning and development. The catch here is, data scientists are required to sort through loads of unstructured data and bring out structured analysis of the same in form of informative dashboards, visualizations, and business-critical metrics.
Owing to this complexity of data, the use of machine learning in data science is pertinent; from this applicability stemmed Deep learning. Deep learning is a part of machine learning but is a tad more efficient, or it would be right to say that deep learning is a focused field under machine learning and uses artificial neural networks or structures modeled based on the human brain to enable a machine to think like a human.
Data science deals with activities like data acquisition, data analysis, data modeling, visualization, and communication. As such, data scientists are adept in computer science, statistics, mathematics, modeling, analytics, and possess a good business sense. With such vast criteria of expertise, they are highly valued and were a rather rare resource until recently.
Applicability and Scope of Data Science
The most basic and first-hand application of data science we all experience is personalization. Personalization of ads as per our search history, personalization of products as per our previous purchases, and such predictive capabilities of data science have brought in advanced personalization of products and services. This ushered in a transformative shift in the e-commerce industry.
Similarly, in healthcare, its predictive modeling is helpful in diagnostics and drug discovery. Powerful image recognition tools assist healthcare professionals to acquire an in-depth understanding of complex medical imagery. As such, natural language processing helps create bots that perform relevant healthcare functions. By using machine learning, pharmaceutical companies can experiment with various genomes and come up with precision medicines.
With the advent of autonomous vehicles, data science has conveniently established itself in the transportation industry. To make the autonomy of vehicles a safe reality, there are many variables to consider; like logistics, optimal delivery routes, allocation of resources, geographic and economic indicators, and consumer profile. Data science makes tackling these many variables a possibility. Apart from product delivery, customer service companies are also reaping benefits from data science. For instance, Uber is making use of this tech for providing a better customer experience and price optimization.
Data science also has applications in the manufacturing industry. Advanced tools of data science help industries monitor their energy costs and optimize the operational and inventory costs. Also, by analyzing customer reviews or product retainment rates in the market, data science enables industries to understand their stand in the market. As technologies like IoT and RPA are entering the manufacturing realm, data science is indispensable in managing the data being captured by them.
The banking and finance industries use data science for predictive and risk modeling. Data science enables banks to detect fraud and manage their large customer database. They can also scrutinize their investment patterns and customer groups to arrive at informed decisions. Like this, banks use real-time analytics to realize the underlying issues impeding their performance. Similarly, finance firms use data science to predict stock values and make data-driven decisions.
Like this, data science has breached every industry in one way or another. This vast scope of data science makes it’s a formidable force in the corporate scene.
The future of Data Science
Experts opine that the demand for data science jobs will increase to range from 364,000 – 2,720,000. This prediction seems plausible owing to the present lack of data scientists and the current requirement in the market.
As technology advances further, the data that needs to be processed and managed will multiply exponentially. This raw data will require proper processing to be converted into meaningful information. So, the future looks prospective enough for both the profession and its professionals.