NumPy Done Right: Speed, Efficiency, and Best Practices (Part 2)

NumPy is a Python library that provides support for multi-dimensional arrays and mathematical functions. When used properly it can be a high effectively tool for scientific computing, data analysis and machine learning.

If you are new to NumPy then attend Part 1 on 9/23. If you are an experienced NumPy user then consider only attending Part 2 on 9/25.

Format: Presentation and hands-on

Learning objectives: Participants will come away with the ability to understand and write effective NumPy code.

Knowledge prerequisites: Basic Python

Hardware/software prerequisites: Participants will need a Python installation on their laptop or a remote system such as Adroit. If an account on Adroit is needed then please request one at least one hour before the workshop.

See the full Research Computing training schedule or subscribe to the Research Computing mailing list.

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Co-hosted with: GradFUTURES

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