In this dissertation, we first present an analysis on the effect of wind at the Blanco Telescope, the home of the Dark Energy Camera (DECam), on Dark Energy Survey (DES) image quality. We find it to have a likely negligible impact on the weak gravitational lensing measurements conducted with images taken during high wind. We then present the methods and validation of two new techniques in weak lensing shear and magnification measurement. We demonstrate highly accurate recovery of weak gravitational lensing shear using an implementation of the Bayesian Fourier Domain (BFD) method, proposed by \cite{BA14}, extended to correct for selection biases. The BFD formalism is rigorously correct for Nyquist-sampled, background-limited, uncrowded image of background galaxies. We conduct initial tests of this code on $\approx 10^9$ simulated lensed galaxy images and recover the simulated shear to a fractional accuracy of $m=(2.1\pm0.4)\times10^{-3},$ substantially more accurate than has been demonstrated previously for any generally applicable shear measurement method. We also introduce a new Bayesian method for selecting high-redshift galaxies and calculating their magnification around foreground lenses. We apply this method to galaxies from DES Science Verification (SV). Finally, we share the results of a survey conducted with DES collaborators on the collaboration itself, in which we find positive attitudes towards education and public outreach (EPO) in physics and astronomy. We also provide recommendations for current and future surveys on how to increase EPO engagement by scientists.