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Development in the field of gas sensors has witnessed exponential growth with a multitude of applications. The diversity of the applications has led to unexpected challenges. Recent advances in data science have addressed the challenges such as selectivity, drift, aging, limit of detection, and response time. The incorporation of modern data analysis including machine learning techniques have enabled a self-sustaining gas-sensing infrastructure without human intervention. This article provides a birds-eye view on data enabled technologies in the realm of gas sensors. While elaborating the prior developments in gas-sensing related data analysis, this article is intended as an entrant for enthusiasts in the domain of data science and gas sensors.