A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the Sloan Digital Sky Survey (SDSS) pipeline. This is because of a combination of three effects: one is simply a matter of defining the scale out to which one integrates the fit when defining the total luminosity, and amounts on average to <= 0.1 mag even for the most luminous galaxies. The other two are less trivial and tend to be larger; they are due to differences in how the background sky is estimated and what model is fit to the surface brightness profile. Using the SDSS sky biases luminosities by more than a few tenths of a magnitude for objects with half-light radii >= 7 arcsec. In the SDSS main galaxy sample, these are typically luminous galaxies, so they are not necessarily nearby. It is shown that PyMorph fits of Meert et al. 2015 to DR7 data remain valid for DR9 images. These findings show that, especially at large luminosities, PyMorph estimates should be preferred to the SDSS pipeline values as they provide a better fit to the surface brightness profiles of galaxies. It is natural to wonder if the difference between PyMorph and SDSS is due to intracluster light (ICL). The effect of PyMorph reductions vs SDSS can be compared to the full sample as in small group environments, and for satellites in the most massive clusters as well to show that the effect is the same. None of these are expected to be significantly affected by ICL. The only additional effect for centrals is found in the most massive haloes, but it is argued that even this is not dominated by ICL. Hence, for the vast majority of galaxies, the differences between PyMorph and SDSS pipeline photometry cannot be ascribed to the semantics of whether or not one includes the ICL when describing the stellar mass of massive galaxies. After checking that PyMorph sky estimates should still be used over SDSS pipeline values, it was found prudent to create the catalog MaNGA PyMorph Photometric Value Added Catalog (MPP-VAC) for the SDSS-IV MaNGA survey galaxies. The MPP-VAC provides photometric parameters obtained from Sersic and Sersic+Exponential fits from PyMorph for the MaNGA DR15 galaxy sample. Moving forward from the initial PyMorph comparisons of this work, the MPP-VAC sample incorporates three improvements: it uses the most recent reduction of SDSS images; it uses slightly modified criteria for determining bulge-to-disk decompositions; and finally, all the fits in MPP-VAC have been eye-balled, and re-fit if necessary, for additional reliability. Its companion catalog, the MDLM-VAC, provides Deep Learning-based morphological classifications for the same galaxies. The morphological classification includes a series of Galaxy Zoo-like questions plus a TType and a finer separation between elliptical (E) and S0 galaxies. Some work in the literature suggests that there is little correlation between the Sersic index of the bulge component and the morphology of these galaxies. The information from the MPP- and MDLM-VACs were combined with MaNGA's spatially resolved spectroscopy to study how the stellar angular momentum depends on morphological type. Strong correlations between stellar kinematics, photometric properties, and morphological type were found. Lastly, this dissertation shows how proper luminosity measurements are imperative for determining the stellar mass function of a galaxy. Additionally, the proper inclusion of M/L gradients within galaxies is imperative to mass estimates. This is of great importance to galaxy formation and evolution models.