Sophisticated data collection requires sophisticated data analysis: every neuroscientist now requires skills in computer programming, statistics, and mathematics [1], [2]. However, most experimental and clinical neuroscientists lack sufficient training in these skills, and the idea of tackling a standard technical course or textbook on data analysis without sufficient tutorials, examples, and hands-on practice is daunting. Therefore, a fundamental gap has emerged in neuroscience labs between highly sophisticated and rich data collection procedures, and limited training to assess these data with state-of-the-art quantitative techniques. While important to training the next generation of neuroscientists, challenges exist in computational neuroscience education [1], compounded by recent requirements for remote learning and virtual classrooms [3].

To help address these challenges, we present here an online, freely available resource to enhance training in neural data analysis:https://mark-kramer.github.io/Case-Studies-Python. To reach the practicing neuroscientist, we assume only a basic knowledge of calculus and statistics, common to those trained in biological sciences [1]. We also invert the standard presentation of data analysis techniques. Typical statistics textbooks in data analysis begin with the development of theory and then describe applications. We instead begin each topic with an example case study of neural data (e.g., action potentials from rat hippocampus or scalp electroencephalogram data recorded from a human subject). These data then motivate the development and application of modern analysis techniques (e.g., visualization approaches, spectral analysis, bootstrapping, and generalized linear models). We emphasize a hands-on approach; example data sets are provided, and computer (Python) code interspersed within the material encourages direct interaction with the concepts. The data, analysis methods and code are not toy examples, but instead correspond directly with modern techniques in use today. Upon completing each case study, the learner will be able to immediately deploy these data analysis tools in his or her own research.