Going big on data

Data Analytics & Big Data Technologies

Machine Learning with R

Ever wondered how Amazon’s recommendation system uncannily understands your requirement and shows you the relevant products you would be like to buy? Or how Shaadi.com shows your matches as per specific needs? Or how bank detect fraudulent credit card transactions? Or how Swiggy or Ola or Uber function? Or how facebook, Google serve you appropriate advertisements? Well, of all of this is done by a breed of savvy professionals called Data Scientists.

The term ‘Data Scientist’ was actually coined in 2008 by D. J Patil and Jeff Hamberbacher, who back then were leaders of data and analytics efforts at LinkedIn and Facebook, respectively. As they were doing a lot of large-scale data analysis and data product development work that role of data analysts or business analysts, they coined the term “Data Scientists” and crowned themselves with this new designation.

In 2012, the Harvard Business review published an article by Dr Patil and Thomas H. Davenport titled “Data Scientists: The sexiest Job of 21st Century” which actually made this designation a mass cult and placed data science amongst the most popular fields to study across the world.

A growing field

Over the past few years, there’s been a great deal of buzz in the media about Data Science, Big Data, Machine Learning, Deep Learning etc. Taking decisions based on large masses of Data not only makes inherent sense, but a strong commercial sense as well. While every organisation, many are still struggling to implement and skilled professionals.

Be that as it may, the science of analysing data has been around for a long time. Companies have been making sense of data from business analysts, statisticians, business consultants, technologists and subject matter experts in business, to collectively solve problems and provide solutions.

Traditional business analysts were trained to draw inference based on structured small data.

However, today large volumes of data, streaming data and unstructured data need analysing and the new breed of data analysts and data scientists are better equipped to do so. It’s safer to say that data scientists are a much more evolved form of data analysts; and for good reason. They’re professionally trained to analyse Big Data and create data products, automated data analytics tools, dynamic visualisation etc.

Demand and prospects

Not surprisingly, data scientists who have domain knowledge and applied skills in various ares like maths, stats, machine learning, deep learning, natural language processing (NLP), R, Python, SQL, SAS, Big Data tools like Hadoop and Spark, visualisation tools like Cogonos, Tableau, click view etc are in demand globally. The application of AI, ML, Deep learning, Data science is growing in India and around the World in almost every industry like telecom, IT, insurance,

manufacturing, healthcare, banking, retail, media, consulting, ecommerce, oil, gas, automobile airline, government, NGOs and start-ups and every functional areas like marketing, finance, operation, HR etc. But while the demand continues to escalate at a rapid pace, Indian academia and industry ois not yet equipped to fulfil the need.
There’s also a growing demand for solutions based on data science, machine learning and AI but sadly there aren’t enough skilled people available to execute these projects skilfully and successfully. The fastest growing roles globally are those of Data Scientists and Advanced Analysts and their demand is expected to spike by 28 percent by 2020. As a result, compared to the market average, it takes five days more for companies to find data scientists and analysts. As a result, employers are even willing to pay premium salaries for professionals with expertise in these areas.

• A Nasscom report states that India needs over 2 lakh Data Scientists.
• Annual Demand for Data Scientists will reach nearly 700,000 by 2020.
• McKinsey predicts a shortage of 105 million data managers by 2018.


Salaries of Chief Data Scientists with 10+ years of experience in mathematics and statistics are at a minimum of Rs 1 crore annually. Unicorn Data Scientists, are still harder to find, and hence enjoy a compensation of more than $200,000 per year. With updated curriculum and skilled facultywho have rel time industry experience, Indian students can equip themselves to become leaders in this exciting field of data science.
Educational qualifications required to be a Data Scientists

Graduates from any stream are qualified to become data scientists as long as they have knowledge and applied skills in all or some fields like maths, stats, machine learning, deep leaning, natural language processing, R, Python, SQL, SAS: visualisation tools like Cognos, clickview, Tableau and Big Data tools like Hadoop, Spark etc. Domain knowledge is a plus. A lot of companies are hiring candidates who possess a master’s degree in statistics, data science, and machine learning etc.

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