  We describe the first season COMAP analysis pipeline that converts raw
detector readouts to calibrated sky maps. This pipeline implements four main
steps: gain calibration, filtering, data selection, and map-making. Absolute
gain calibration relies on a combination of instrumental and astrophysical
sources, while relative gain calibration exploits real-time total-power
variations. High efficiency filtering is achieved through spectroscopic
common-mode rejection within and across receivers, resulting in nearly
uncorrelated white noise within single-frequency channels. Consequently,
near-optimal but biased maps are produced by binning the filtered time stream
into pixelized maps; the corresponding signal bias transfer function is
estimated through simulations. Data selection is performed automatically
through a series of goodness-of-fit statistics, including $\chi^2$ and
multi-scale correlation tests. Applying this pipeline to the first-season COMAP
data, we produce a dataset with very low levels of correlated noise. We find
that one of our two scanning strategies (the Lissajous type) is sensitive to
residual instrumental systematics. As a result, we no longer use this type of
scan and exclude data taken this way from our Season 1 power spectrum
estimates. We perform a careful analysis of our data processing and observing
efficiencies and take account of planned improvements to estimate our future
performance. Power spectrum results derived from the first-season COMAP maps
are presented and discussed in companion papers.
