Use Python to model decisions, optimize under constraints, and evaluate trade-offs and uncertainty.
Created by python_scripts (Abhijith Warrier).
Learn to turn practical questions into explicit objectives, constraints, and alternatives. Use Python to solve models, compare outcomes, and examine how assumptions influence a decision.
The following topics describe the planned scope of the series. This roadmap is not a list of already published notebooks.
| Area | Concepts |
|---|---|
| Problem formulation | Decision variables, objectives, constraints, feasibility, and optimality. |
| Resource allocation | Linear programming, integer programming, assignment, and knapsack models. |
| Planning and coverage | Scheduling, transportation, and set cover. |
| Trade-offs and uncertainty | Sensitivity analysis, Pareto trade-offs, expected outcomes, value of information, and robust optimization. |
Once this repository and its notebooks are available, clone it:
git clone https://github.com/abhijithwarrier/Python-Decision-Science.git
cd Python-Decision-ScienceCreate and activate a virtual environment:
python -m venv .venvmacOS / Linux:
source .venv/bin/activateWindows PowerShell:
.venv\Scripts\Activate.ps1Install and launch JupyterLab:
python -m pip install jupyterlab
python -m jupyterlabUse python3 instead of python where required by your system. Open a notebook and run its cells from top to bottom. Any topic-specific packages and Python-version requirements will be stated in that notebook; install packages using its dedicated installation section.
A computed solution is only as useful as its model. Examples will state assumptions, report solver status where relevant, and distinguish proven optimal solutions from heuristics or feasible candidates.
Run the example, change an input or assumption, and explain why the result changed. Keep the conceptual lesson connected to the code and its output.
Part of the python_scripts educational series: learning concepts by implementing and exploring them with Python.
Follow @python_scripts on Instagram for accompanying Reels and explanations.
Created by Abhijith Warrier
