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Neuroimaging and Data science

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.


Topics covered in this lesson
  • The Data Science Toolbox
    • The Unix operating system
    • Version control
    • Computational environments and computational containers
  • Programming
    • Introduction to Python
    • The Python environment
    • Sharing code with others
  • Scientific Computing
    • The scientific Python ecosystem
    • Manipulating tabular data with Pandas
  • Neuroimgaing in Python
    • Reading and aligning neuroimgaing data with Nibabel
    • The Brain Imaging Data Structure (BIDS)
  • Image Processing
    • Image processing
    • Image segmentation
    • Image registration
  • Machine Learning
    • The core concepts of machine learning
    • The scikit-learn package
    • Overfitting
    • Validation
    • Model selection
    • Deep learning
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