Subject
9 entries
Scientific Computing
Bookmarks
Exploratory Computing with Python — Mark Bakker
Mark Bakker's open course on exploratory computing with Python — Jupyter notebooks covering NumPy, Matplotlib, and scientific computing techniques. Aimed at engineers and scientists who want to use Python for quantitative analysis without a computer science background.
Getting the Best Performance out of NumPy
Featured recipe from the IPython Cookbook on getting the best performance out of NumPy — covering vectorization, broadcasting, memory layout, and avoiding Python loops. The kind of practical optimization guide that separates slow scientific Python from production-grade numerical code.
100 Numpy Exercises
Nicolas Rougier's 100 exercises for NumPy, ranging from beginner to expert, covering the array operations that make NumPy indispensable. One of the most effective ways to internalize NumPy's vectorization mindset.
Research Computing Meetup Fall 2013
GitHub repo of materials from the Research Computing Fall 2013 meetup series — IPython notebooks covering Python for scientific computing, parallel processing, and HPC workflows.
A Gallery of Interesting IPython Notebooks
Curated GitHub wiki of interesting IPython Notebooks covering scientific computing, data analysis, machine learning, and visualization. The 2014 canonical list of notebooks worth running — before nbviewer and Binder made sharing notebooks routine.
The Homogenization of Scientific Computing: Why Python Is Eating Other Languages' Lunch
R-Bloggers post arguing that Python was converging on R and MATLAB's territory in scientific computing in 2014. The key insight: Python didn't win by being better at any one thing, but by being good enough at everything while sharing one ecosystem.
Parakeet: A Faster Python for a Better Tomorrow
Parakeet was a Python JIT compiler targeting NumPy array operations, promising to make numerical Python code run at near-native speeds without rewriting in C or Cython. An early attempt at the problem that Numba later solved more completely.
Python Extensions to Do Machine Learning
A roundup of Python extensions and libraries for machine learning circa 2013 — the ecosystem before it had fully consolidated around scikit-learn, NumPy, pandas, and matplotlib as the canonical stack.
Quantitative Economics (quant-econ.net)
quant-econ.net is Thomas Sargent and John Stachurski's free online course in quantitative economics using Python — covering dynamic programming, stochastic processes, and economic modeling. Nobel laureate-authored open curriculum before that was common.