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Although our understanding about galaxy evolution has improved in the past few decades, we still do not understand how galaxies suddenly stop forming stars and move towards a quiescent phase. In order to do that, we must derive the Star Formation Histories (SFHs) of galaxies, that trace the change in Star Formation (SF) inside the galaxy over the cosmic timescale. This is achieved by using a set of spatially resolved near-ultraviolet (NUV) and optical spectroscopic images of the galaxies. We generate the Swift/UVOT + MaNGA value added catalog (SwiM VAC; Molina et al., 2020b) which comprises 150 galaxies having a combination of both, optical spectroscopy and NUV imaging. We design a Markov Chain Monte Carlo (MCMC) Spectral Energy Distribution (SED) fitting algorithm that fits non-parametric SFHs to the SwiM VAC galaxies using the BC03 (Bruzual and Charlot, 2003) Simple Stellar Population (SSP) models. We find that the red sequence galaxies have assembly times earlier than the blue cloud galaxies. We also find that high mass galaxies show negative metallicity spatial gradients with an inside-out mass assembly time. Furthermore, we discover inconsistencies in the BC03 models which could be due to flux calibration issues with the model library. This suggests that along with high SNR in the data, one also needs to use updated model libraries to accurately derive SFHs of galaxies and derive meaningful insights about galaxy evolution., © 2022 Nikhil Ajgaonkar