Companies all over the United States are scrambling to hire data scientists. LinkedIn says about 150,000 are needed just to fill the open slots this year. But qualified data scientists cannot be simply conjured out of thin air.
Adam Lichtl contends that $40 online courses do not a data scientist make in this article published in The Next Web:
…a career in data science should be likened to any skilled profession that requires advanced training, education, and experience — such as a doctor, lawyer, or architect. Each of these requires not only a basic aptitude for the job, but also a massive amount of training, education, and knowledge gained through on-the-ground experience.
Data science should be approached with the same amount of rigor, as this role can be responsible for providing the data-driven basis for very real, very expensive business decisions.
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A data scientist finds the stories that the data are telling, and separates true patterns and trends from randomness, just as Kepler did when he studied planetary motion. The job essentially lies at the intersection of probability/statistics and software engineering.
Because data points often flow from multiple fragmented and noisy sources, the data scientist must understand the context of the data, set up pipelines to integrate and clean the data, and apply rigorous statistical methods to coax out what the data are trying to tell us.
It is vital that they do their job correctly and accurately because, as mentioned above, the stories that data scientists weave from the data feed directly into business decisions connected to real money and risk. Done correctly, data science can be a transformative role within any organization, converting abstract data sitting in databases into real-world insights and effective actions.
