Python for Neuroscience

Participants will first learn the core building blocks of Python, including variables, data types, operators, collections, conditional statements, loops, and functions, developing the ability to translate simple problems into structured computational solutions. The course then introduces NumPy for efficient numerical computing, covering arrays, vectorized operations, matrix computations, and the representation of scientific data such as neural activity vectors, firing rates, and connectivity matrices. Building on these foundations, participants will learn to use Pandas for working with real-world datasets, including loading CSV and Excel files, organizing data with DataFrames, filtering observations, grouping data, and calculating summary statistics. Throughout the course, practical examples drawn from scientific and neuroscience contexts will demonstrate how Python can be used to organize, process, analyze, and interpret experimental data. By the end of the course, students will have the programming and data-analysis foundations needed to confidently work with scientific datasets and progress toward more advanced computational neuroscience, machine learning, and data science workflows.