Scientific Computing

Scientific Computing#

In the past weeks, we covered essential Python concepts like control flow, functions, and classes. Now, we’ll dive into practical applications, focusing on key libraries for scientific computing, which are crucial for working with data in fields like EEG, fMRI, behavioral analysis, and questionnaires.

What is Scientific Computing?#

Scientific computing refers to using programming and data analysis techniques to process and analyze scientific data. For your Master’s course, this includes a broad range of data types, from brain imaging (like fMRI and EEG) to behavioral studies and questionnaires.

Python is a powerful tool for these tasks because it has a wide variety of libraries developed by the research community, students, and organizations. Think of it as an ecosystem where various libraries compete for your attention, each aiming to meet your needs. While multiple libraries may solve similar problems, the Python community has settled on a few core packages that are commonly used.

In today’s session, we’ll explore three key libraries:

  • Numpy for numerical computing

  • Pandas for handling tabular data

  • Matplotlib for creating visualizations

By mastering these, you’ll build a solid foundation for working with scientific data in Python.

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