Examples: Computational Teaching#
Linear Algebra (UofA)#
Michael O’Brien (mbeaulism)
Linear algeabra labs with GeoGebra
MICA (BrockU)#
Chantal Buteau (tanguera-TO)
Math coding course for math majors and future math teachers with math projects in Python and introductory ‘school’ activities in Scratch
PICUP#
Danny Caballero (dannycab)
Partnership for Integration of Computation into Undergraduate Physics
Calculus (ULeth)#
Sean Fitzpatrick (sean-fitzpatrick)
Python and Jupyter notebooks for calculus
Linear Algebra (ULeth)#
Sean Fitzpatrick (sean-fitzpatrick)
Interactive PreTeXt book on linear algebra with Python
Machine Learning (UofC)#
Matthew Greenberg (mgreenbe)
Machine learning for data science in Jupyter notebooks
Calculus and Probability (UWO)#
Kelvin Chan (ktychan)
First-year calculus and probability course with Python assignments in Jupyter notebooks
Coding, Computational Modelling & Equity in Mathematics Education#
Ameanda Chow with Miroslav Lovric et al. (amutoronto)
Coding, Computational Modelling & Equity in Mathematics Education - PD Day and Symposium
Working group “Coding and computational modelling in secondary and university mathematics”
Mathematical Computing (UBC)#
Patrick Walls (patrickwalls)
Introduction to mathematical computing with Python and Jupyter including basic programming, numerical integration, numerical methods for differential equations, linear systems, eigenvalues and eigenvectors
Mathematical Modelling (UBC)#
Patrick Walls (patrickwalls)
mathematical modelling with ordinary differential equations, probability distribiutions and linear/logistic regression (with mathematical computing as a prerequisite)
Applied Linear Algebra (UBC)#
Patrick Walls (patrickwalls)
Second course in linear algebra including matrix decompositions LU, QR and SVD, and the discrete Fourier transform with computations in Python and Jupyter (but assumes no prior knowledge)