If you donâ€™t have a Data Scientist on your team already, chances are you will soon.
According to a recent India Jobs study, data science is the fastest-growing field in India. As every company turns into a technology company, organizations in industries such as manufacturing, healthcare and retail are all looking to strengthen their data teams. India is emerging as one of the worldâ€™s largest data science capitals, with companies like Mercedes-Benz, Walmart, PayPal and AIG setting up data science centers in the country. Research from the Everest Group shows that India holds between 35 percent and 50 percent of the global analytics services market.
The number of analytics jobs nearly doubled from April 2016 to April 2017, and there are almost 50,000 analytics and data jobs currently open in the country. Hiring companies are looking for people skilled in tools such as R, Python and Hadoop.
How will Indian companies find the talent to fill this demand for data? What challenges do hiring managers face? And how can companies re-skill and up-skill current employees to fill the demand?
Keeping Pace with Emerging Skills
While thereâ€™s an army of IT workers in India, many need to be taught new skills in emerging tools like R and Python. NASSCOM, the Indian IT trade association, expects that almost 50% of the IT workforce will need to learn these new technologies to avoid becoming redundant.
Even if companies can hire the data talent they need, responding to the data crunch will take more than just quick hiring. In a 2017 report about our current â€œage of analytics,â€� authors from McKinsey & Co. warn: â€œAdapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.â€�
â€œThere are a lot of people in India who are already very well-trained with quantitative, mathematical, and statistical skills,â€� says Dr. Nilanjan Chatterjee, Bloomberg Distinguished Professor at Johns Hopkins University. â€œBut if you really want to be a data scientist, you need cutting-edge skills. You canâ€™t be afraid to attack the data and get your hands dirty.â€�
Leveraging Online Learning to Keep Up
At Coursera, weâ€™ve seen enterprise customers in India seek out data science training for teams. One of the most popular paths is the Coursera Data Science Specialization, a nine-course introduction to data science created by leading professors from Johns Hopkins University. One course, The Data Scientist’s Toolbox, stands out with the highest enrollment. The course pairs a conceptual introduction to the ideas behind data science with a practical introduction to important tools like version control, markdown, git, GitHub, R, and RStudio.
Dr. Chatterjee explains that data science is a broad field, and requires people with different skill sets and focus areas. For example, talent with a background in computer science might be most interested in database issues like storing and managing large data sets. Others with a statistical background might excel in analysis, but look for help from others when it comes to data management and quality control. In this booming field, thereâ€™s room for many different focus areas and backgrounds.
Preparing for the Future of Data Science in India
Training an entire workforce of IT talent on the skills of tomorrow will take a joint effort from organizations and individuals. Businesses will need to train and hire not just for todayâ€™s needs, but for the needs of the future. While training a new generation and keeping up with emerging tools is a challenge, data science has the potential to become a key engine of economic growth in India over the next decade. NASSCOM predicts that the current push to reskill and upskill tech workers will translate to 1.5 million to 2 million people working on next-gen technologies in India in the next 5 years.
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