FreeCampus Python
Scientific Computing with Python
Represent numerical problems, run experiments, visualize results, and apply scientific algorithms.
scientific-python
scientific-computing
Course progress
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WarningCourse in development
The current scientific lessons are available, while deeper numerical workflows, the capstone, assessment, and certificate are still being developed.
ImportantPrerequisite
Complete Python Foundations. Statistics is a parallel pathway rather than a prerequisite, so you may take the two courses in either order.
0%current material
Before you start
- Designed for: learners who want to investigate numerical or scientific questions with code.
- You are ready when: you can use Python variables, collections, loops, and functions and can explain the output of a small program.
- Practice setup: Google Colab is sufficient for the current NumPy, Matplotlib, SymPy, and SciPy examples. A local environment is optional but useful for larger reproducible experiments.
What the current lessons teach
- represent numerical data with NumPy arrays;
- model uncertainty with repeated random simulations;
- create and explain simple plots with Matplotlib;
- distinguish symbolic mathematics from numerical computation;
- identify where SciPy algorithms fit a scientific workflow.
Current unit
Scientific Python
Use NumPy, random simulation, Matplotlib, SymPy, and SciPy in small experiments.
Planned expansion
- floating-point error, numerical stability, tolerances, and reproducible computation;
- broadcasting, masking, memory layout, and multidimensional arrays;
- numerical linear algebra, integration, interpolation, optimization, and differential equations with SciPy;
- labelled multidimensional data and metadata with xarray;
- scientific formats and storage patterns including NetCDF, HDF5, and Zarr;
- contour plots, heatmaps, error bars, time series, log scales, and accessible multipanel figures;
- measurement-led performance work using vectorization, profiling, chunking, lazy computation, Numba, and Dask;
- an introduction to parallel jobs, clusters, MPI, and the boundary where HPC tools become appropriate;
- a reproducible scientific project that records inputs, parameters, numerical checks, and limitations.
Practice, assessment, and next step
The current lessons provide runnable experiments, Colab notebooks, and formative quizzes. They count toward local progress, but a scientific capstone, rubric, and certificate assessment are still planned. Scientific Computing and Statistics are both prerequisites for Data & ML, but neither must come first.