After factoring job satisfaction, salary, and openings, Glassdoor ranked Data Science careers #1 (with a Job Score of 4.8/5) and determined the average salary of a Data Scientist to be $118,709. Moreover, McKinsey estimates that market demand for such talent will dramatically outpace the supply – leaving as many as 190,000 unfilled positions in 2018 (in the U.S. alone). Companies are scrambling to find those rare individuals with the ability to synthesize complex math, computer science, engineering, and creativity. Do a quick search of any job board, and you will see such positions posted (and re-posted) daily. INSOFE – a globally-recognized Data Science institute established to cultivate quantitative and Machine Learning skills, in tandem with Soothsayer Analytics – a US-based Data Science & Artificial Intelligence firm, are jointly offering an innovative educational experience that synthesizes world-class education with real-world experience.

This involves grouping different data points (customers, products, movies, etc.) Hierarchical clustering is based around organizing data points into a set of similar clusters, then recursively grouping clusters together until you are left with a single cluster. Because the algorithm has to run through every data point and compare groups of data points to other groups of data points, the run time increases dramatically. Usually the algorithm progresses by randomly assigning data points as centroids, followed by assigning data points to the appropriate clusters.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind. This challenge (and opportunity) is not limited to certain industries.