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Scanning transmission electron microscopy (STEM) is one of the most popular materials science methods to characterize the structure and chemistry of nanoscale samples, owing to its high resolution and many flexible operating modes. In a conventional STEM experiment, we focus the electron beam down to a probe from nanometer to sub-angstrom scale, and scan it over the sample surface while recording diffracted signals which are transmitted through the specimen. STEM can also record analytic signals such as x-rays generated by the electron beam to measure composition, or energy loss of the transmitted electrons to probe the electronic structure of samples. Conventional STEM imaging detectors experiments produce only a few intensity values at each probe position, but modern high-speed detectors allow us to measure a full 2D diffraction pattern, over a grid of 2D probe positions, forming a four dimensional (4D)-STEM dataset. These 4D-STEM datasets record information about the local phase, orientation, deformation, and other parameters, for both crystalline and amorphous materials. 4D-STEM datasets can contain millions of images and therefore require highly automated and robust software codes to extract the target properties. In this talk, I will introduce our open source py4DSTEM analysis toolkit, and show how we use these codes to perform data-intensive studies of material properties over functional length scales. I will also demonstrate some applications of modern machine learning tools, to perform measurements on electron diffraction patterns where property signals have been scrambled by multiple scattering of the electron beam.