Data science methods and tools-including programming, data management, visualization, 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 analyze 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, visualization, 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.
* Fills the need for an authoritative resource on data science for neuroimaging researchers
* Strong emphasis on programming
* Provides extensive code examples written in the Python programming language
* Draws on openly available neuroimaging datasets for examples
* Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process
Rezensionen / Stimmen
"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
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Illustrationen
Maße
Höhe: 253 mm
Breite: 175 mm
Dicke: 25 mm
Gewicht
ISBN-13
978-0-691-22275-2 (9780691222752)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
