Data Science for Neuroimaging: An Introduction

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Data science methods and tools including programming, data management, visualisation and machine learning and their application to neuroimaging research.

As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage and analyse datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools and approaches that facilitate automated, reproducible and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualisation and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions.

Reviews

"I would absolutely recommend this book, not just for those wanting to do neuroimagining analyses, but for anyone who wants to do any serious scientific computing using Python. The well-selected exercises ensure that both undergraduate and graduate students will find engaging and thorough learning experiences throughout this book."---Jonathan Shock, Mathemafrica

Author Bio

Ariel Rokem is research associate professor at the University of Washington Department of Psychology and Data Science Fellow at the University of Washington eScience Institute. He is a contributor to Python open-source tools for scientific computing and directs the NIH-funded Summer Institute for Neuroimaging and Data Science. Tal Yarkoni is a data scientist and research professor in the Department of Psychology at the University of Texas at Austin. His academic work focuses on developing new tools and methods for the analysis of psychology and neuroimaging data.