Numerical Analysis with Numpy

This course introduces participants to the Numpy library and explores how to use it to write faster, more manageable code for crunching large numerical datasets.


Tech Analysts, S&T Analysts (optionally)

Learning Outcomes

By the end of this course, participants should be able to:rn• Describe and deploy the core functionality of Numpyrn• Compare its features and performance to similar methods native to Pythonrn• Select the best approach (Numpy or not Numpy) based on performance, memory use and other considerations

Course Content

• Overview: why Numpy?rn• Similarities with and differences from the basic math library.rn• Creating and transforming arrays with ndarray.rn• Comparing ‘vectorised’ Numpy operations on large arrays with Python-coded equivalents in base libraries.rn• Statistical functionality using NumPy’s distribution and random number generator.rn• Workshop: generate random walks in Numpy and compute volatility, risk/return ratios and other statistical outputs.


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