Data Science for Neuroimaging – An Introduction

Name: Data Science for Neuroimaging – An Introduction
Brand: Princeton University Press
SKU: 9780691222738
Price: 50407 HUF
Availability: InStock

Szerző: Ariel Rokem, Tal Yarkoni

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Kötés: Kemény kötésű

Kiadó: Princeton University Press

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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 - Kemény kötésű

Kiadva

2023

oldal

272

EAN

9780691222738

ISBN

0691222738

Enbook ID

42869647

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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Data Science for Neuroimaging – An Introduction

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