  Continuous developments in data science have brought forth an exponential
increase in complexity of machine learning models. Additionally, data
scientists have become ubiquitous in the private market, academic environments
and even as a hobby. All of these trends are on a steady rise, and are
associated with an increase in power consumption and associated carbon
footprint. The increasing carbon footprint of large-scale advanced data science
has already received attention, but the latter trend has not. This work aims to
estimate the contribution of the increasingly popular "common" data science to
the global carbon footprint. To this end, the power consumption of several
typical tasks in the aforementioned common data science tasks will be measured
and compared to: large-scale "advanced" data science, common computer-related
tasks, and everyday non-computer related tasks. This is done by converting the
measurements to the equivalent unit of "km driven by car". Our main findings
are: "common" data science consumes $2.57$ more power than regular computer
usage, but less than some common everyday power-consuming tasks such as
lighting or heating; large-scale data science consumes substantially more power
than common data science.
