The Data Analysis Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey:
  Overview
  Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is acquiring
integral-field spectroscopy for the largest sample of galaxies to date. By
2020, the MaNGA Survey --- one of three core programs in the fourth-generation
Sloan Digital Sky Survey (SDSS-IV) --- will have observed a statistically
representative sample of 10$^4$ galaxies in the local Universe
($z\lesssim0.15$). In addition to a robust data-reduction pipeline (DRP), MaNGA
has developed a data-analysis pipeline (DAP) that provides higher-level data
products. To accompany the first public release of its code base and data
products, we provide an overview of the MaNGA DAP, including its software
design, workflow, measurement procedures and algorithms, performance, and
output data model. In conjunction with our companion paper Belfiore et al., we
also assess the DAP output provided for 4718 observations of 4648 unique
galaxies in the recent SDSS Data Release 15 (DR15). These analysis products
focus on measurements that are close to the data and require minimal
model-based assumptions. Namely, we provide stellar kinematics (velocity and
velocity dispersion), emission-line properties (kinematics, fluxes, and
equivalent widths), and spectral indices (e.g., D4000 and the Lick indices). We
find that the DAP provides robust measurements and errors for the vast majority
($>$99%) of analyzed spectra. We summarize assessments of the precision and
accuracy of our measurements as a function of signal-to-noise, and provide
specific guidance to users regarding the limitations of the data. The MaNGA DAP
software is publicly available and we encourage community involvement in its
development.
