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Continuous developments in data science have brought forth an exponentialincrease in complexity of machine learning models. Additionally, datascientists have become ubiquitous in the private market, academic environmentsand even as a hobby. All of these trends are on a steady rise, and areassociated with an increase in power consumption and associated carbonfootprint. The increasing carbon footprint of large-scale advanced data sciencehas already received attention, but the latter trend has not. This work aims toestimate the contribution of the increasingly popular "common" data science tothe global carbon footprint. To this end, the power consumption of severaltypical tasks in the aforementioned common data science tasks will be measuredand compared to: large-scale "advanced" data science, common computer-relatedtasks, and everyday non-computer related tasks. This is done by converting themeasurements to the equivalent unit of "km driven by car". Our main findingsare: "common" data science consumes $2.57$ more power than regular computerusage, but less than some common everyday power-consuming tasks such aslighting or heating; large-scale data science consumes substantially more powerthan common data science.

How sustainable is "common" data science in terms of power consumption?