Document Type
Article
Version
Final Published Version
Publication Title
The Journal of Computational Science Education
Volume
15
Publication Date
2024
Abstract
Computation is a significant part of the work done by many practicing scientists, yet it is not universally taught from a scientific perspective in undergraduate science departments. In response to the need to provide training in scientific computation to our students, we developed a suite of self-paced “modules” in the form of Jupyter notebooks using Python. These modules introduce the basics of Python programming and present a wide variety of scientific applications of computing, ranging from numerical integration and differentiation to Fourier analysis, Monte Carlo methods, parallel processing, and machine learning. The modules contain multiple features to promote learning, including "Breakpoint Questions," recaps of key information, self-reflection prompts, and exercises.
Citation
Matlin, Mark. 2024. “Scientific Computation in Jupyter Notebooks Using Python.” The Journal of Computational Science Education 15 (2): 24–28. https://doi.org/10.22369/issn.2153-4136/15/2/5.
DOI
https://doi.org/10.22369/issn.2153-4136/15/2/5
Computational Module 0: Introduction to Computational Methods for the Sciences
Computational_Module-01A-PythonIntro.ipynb (93 kB)
Computational Model 1a: Python Intro
Computational_Module-01B-PythonIntro.ipynb (405 kB)
Computational Model 1b: Python Intro
Computational_Module-01C-PythonIntro.ipynb (43 kB)
Computational Model 1c: Python Intro
Computational_Module-02-ErrorsSpeed.ipynb (38 kB)
Computational Model 2: Error Speed
Computational_Module-03-Iterative_Methods.ipynb (52 kB)
Computational Model 3: Iterative Methods
Computational_Module-04-Differentiation.ipynb (46 kB)
Computational Model 4: Differentiation
Computational_Module-05-Integration.ipynb (45 kB)
Computational Model 5: Integration
Computational_Module-06-Linear_Equations.ipynb (43 kB)
Computational Model 6: Linear Equations
Computational_Module-07-Eigenequations.ipynb (52 kB)
Computational Model 7: Eigen Equations
Computational_Module-08-DataAnalysisVisualization.ipynb (406 kB)
Computational Model 8: Data Analysis Visualization
Computational_Module-09-FourierAnalysis.ipynb (883 kB)
Computational Model 9: Fourier Analysis
Computational_Module-10-DifferentialEquations.ipynb (55 kB)
Computational Model 10: Differential Equations
Computational_Module-11-PDEs.ipynb (88 kB)
Computational Model 11: PDEs
Computational_Module-12-MonteCarloMethods.ipynb (56 kB)
Computational Model 12: Monte Carlo Methods
Computational_Module-13-SymbolicComputation.ipynb (58 kB)
Computational Model 13: Symbolic Computation
Computational_Module-14-OOP.ipynb (130 kB)
Computational Model 14: OOP
Computational_Module-15-ParallelComputing.ipynb (139 kB)
Computational Model 15: Parallel Computing
Computational_Module-16-MachineLearning-v1.3.ipynb (210 kB)
Computational Model 16: Machine Learning v. 1.3.
Instructors_Guide.pdf (736 kB)
Instructor's Guide