Scientific Python and Optimisation for Research

Computational research should be reproducible

Scientific Python can connect data processing, numerical models, optimisation and visualisation in one workflow. A research implementation should make inputs, transformations, parameter choices and outputs traceable.

For optimisation work

The formulation matters before the algorithm: define the objective, decision variables, constraints and evaluation method first. Then choose a numerical approach that fits the structure and cost of the problem.

Review areas

• Data provenance and preprocessing.
• Units, numerical precision and edge cases.
• Objective functions and constraints.
• Random seeds or stochastic procedures where relevant.
• Train/test or calibration/validation separation where data-driven models are involved.
• Sensitivity of conclusions to parameter choices.

Code quality supports scientific quality when it makes the analysis easier to inspect, reproduce and challenge.