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Könyv Data Science for Neuroimaging – An Introduction Ariel Rokem

Data Science for Neuroimaging – An Introduction

Szerző: Ariel Rokem, Tal Yarkoni
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Princeton University Press
Elérhetőség: Beszállítói készleten
Küldés 14-21 napon belül
20 540 Ft
Data science methods and tools—including programming, data management, visualization, and machine le...

Információk a könyvről

Szerző
Ariel Rokem, Tal Yarkoni
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2023
oldal
272
EAN
9780691222752
ISBN
0691222754
Enbook ID
42869648
Kiadó
Princeton University Press
Súly
666
Méretek
177 x 254 x 15

Teljes leírás

Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging researchAs 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

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