Computer languages over the years tend to rise and fall in popularity, depending upon what the job market looks like, what's the hot technology du jour, and what needs it fulfills. There was a time in the not-so-distant past when Ruby on Rails was the must-have language out there, yet Ruby now seldom cracks the top 20 languages in most people's surveys. I can even remember a time when LISP was the dominant language in the artificial intelligence space, though you're more likely today to find LISP only as faint echoes in languages like Erlang and Clojure. If you look through older articles on DSC you'll find plenty of fodder about whether R or Python is the better language to learn, though by the numbers Python looks to be eclipsing R finally in the great language religious wars. However, the reality is that in the analytics space, your language choice is becoming less and less relevant.

Data scientists help companies make solid data-backed decisions. This job also happens to be the fastest growing job in the United States, according to LinkedIn. Data science job role has witnessed a growth of 6.5 times from 2012 and there are more than 6,000 data scientists jobs currently listed on LinkedIn. While the job market continues to grow, the demand for data scientists directly results from the shortage of workers. As per a report by McKinsey, we might soon see a shortage of up to 250,000 data scientists.

Advancements in analytics technologies and convergence of technologies have led to transformational breakthroughs in the business landscape, enabling companies to gain a competitive advantage and create new ways of generating revenues. The availability of ever-increasing amounts of structured and unstructured data, along with higher, cheaper and faster computing power, has been essential for the development of advanced analytics. The Internet of Things (IoT) has also boosted the development of the streaming analytics market through sensor-driven, real-time insights into consumer behaviour. Frost & Sullivan's recent analysis, Advanced Analytics: Disruptive Opportunities, reveals the different types of analytics technologies, the potential applications and industries impacted, use cases, future technology convergence scenarios, the patent scenario, emerging business models, and new revenue streams. "It's clear that artificial intelligence, in particular, is disrupting the analytics space, creating new opportunities in healthcare, agriculture, retail, advertising, media, automotive, insurance, banking, finance, customer service, surveillance, gaming, education, and smart homes," said Kiran Kumar, TechVision Program Manager at Frost & Sullivan. For example, analytics will be hugely disruptive to the healthcare space, greatly accelerating the drug discovery process.