This repository is a companion to the textbook Case Studies in Neural Data Analysis, by Mark Kramer and Uri Eden. That textbook uses MATLAB to analyze examples of neuronal data. The material here is similar, except that we use Python. The intended audience is the practicing neuroscientist - e.g., the students, researchers, and clinicians collecting neuronal data in the hospital or lab. The material can get pretty math-heavy, but we’ve tried to outline the main concepts as directly as possible, with hands-on implementations of all concepts. We focus on only two main types of data: spike trains and electric fields (such as the local field potential [LFP], or electroencephalogram [EEG]). If you’re interested in other data (e.g., calcium imaging, or BOLD), you may still find the examples indirectly useful (for example, demonstrations of how to compute and interpret a power spectrum of a signal).