Overview

ata Science with R: Bridging Brain Research and Statistics is a comprehensive guide for researchers, students, and professionals who seek to explore the brain through the lens of data science. Combining the rigor of neuroscience with the versatility of R programming, this book equips readers to handle, analyze, and model neurobiological data across a wide range of applications. From EEG time series and fMRI brain maps to statistical modeling and machine learning, each chapter introduces core neuroscience concepts alongside step-by-step R implementations. With an emphasis on reproducibility, open science, and ethical practices, the book demystifies neurodata workflows and encourages readers to build their own pipelines-from data wrangling to publication-ready visualization. Readers will learn how to: Load and preprocess neuroimaging formats (e.g., NIfTI, BIDS) Analyze electrophysiological data using time-frequency tools Build statistical models for cognitive and behavioral signals Apply machine learning to decoding and classification tasks Visualize brain networks, cortical activity, and connectivity Use R packages like tidyverse, ggseg, fslr, brms, and tidymodels This book is ideal for neuroscience students transitioning into data science, data scientists entering neuroinformatics, or anyone seeking to conduct reproducible, statistically sound brain research using R. Whether you're working with experimental lab data, public repositories, or clinical EEG/fMRI scans, NeuroData Science with R serves as your practical companion in navigating the complex but fascinating frontier between brain and data.

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