Data Science, as the name suggests, is the science of extracting information and knowledge from a large amount of data by using algorithms and processes. This knowledge is then used to gain insights about a company and is utilized by analysts to create predictive business models and to make critical business decisions.
In the past decade or two, with everything online, the major problem faced by most businesses was the management of data. Huge heaps of data needed to be stored in a structured way, so that they could be extracted and referred to when needed. But this wasn’t possible with the continuously doubling volumes of digital data.
These extremely huge and complex data sets or ‘Big data’ as it is commonly called couldn’t be processed by traditional data processing software. There was a dire need to formulate techniques and approaches to structure, segment, and cluster the big data.
And hence, data science became the need of the hour. Data science uses a computational framework of statistics, mathematics, programming, and structuring to sort and analyze the data so as to glean relevant insights from it.
A data scientist uses a mixed bag of skills pertaining to statistics, machine learning, classification, clustering analysis, data mining and information sciences. Up until a couple of years back, firms used to rely on the power of human predictability, estimation, and a basic gut feeling of experts in the field, in order to make important business decisions and take calculated risks.
Naturally, such decisions were not full proof and were subject to human error resulting in losses. But now with the advent of science and analytics, they have finally found a means to scale, sort and analyze their data, and create predictive business models to enable sound decision making.
Detailed statistical and empirical analysis of data results in effective risk-taking that multiplies profits and ensures smooth functioning and success of the organization. Needless to say, data scientists are very sought after and have become indispensable in the last few years. It is no wonder then, that for the past 3 years running, ‘Data scientist’ was listed as the highest paid job in the US by Glassdoor.com with a median base salary of $105,000 with over 4000 job openings. LinkedIn’s 2017 U.S. emerging job report showed that there are 9.8 times more machine learning engineers working today than 5 years ago.
Even in India, glassdoor.com states that the average base pay for a data scientist is Rs. 6,50,000/- per year with hundreds of openings. To be eligible for such a job, you need at least a bachelor’s degree in computational software sciences, knowledge about statistical languages like R, experience in machine learning, and knowledge of frameworks like Java, Apache Hadoop, and Python.
In addition to this, data visualization skills are a must. It is not just important to extract and analyze the data, but it’s also essential to be able to communicate your findings via proper visualization methods. All in all, companies look for an experienced data-driven problem solver with excellent analytical skills.
Data scientists work along with data analysts and at times may even cross over to the business-driven analytics side wherein their findings can be examined from a tactical business point of view. With more and more people opting for master’s degree in data analytics and big data management courses, and more and more job openings everywhere, it’s evident that this new wave of data scientists is here to stay.