I have a set of physics-journal datasets and several higher-order theoretical equations that I need handled entirely in Python. The job is two-fold: first, deliver working code that plots line graphs, bar charts, and scatter plots drawn straight from the papers; second, build numerical routines that solve my higher-order equations with solid, peer-review-ready accuracy. I expect the core stack—NumPy, SciPy, and Matplotlib—to be used, and I’m happy for you to introduce any additional scientific libraries if they streamline the workflow or boost performance. All scripts or notebooks must be fully commented so I can trace every step later. Alongside the code, I want a short, hands-on learning component focused on the data-science aspects of Python: explanations of your approach, quick walkthroughs of the plotting functions, and a clear breakdown of the numerical methods you choose. Think of it as mini-tutorials woven into the delivery rather than formal lectures. Deliverables • Clean, executable Python files or Jupyter notebooks that: – read my provided datasets, – produce publication-quality line, bar, and scatter plots, and – solve the specified higher-order theoretical equations numerically. • Inline comments plus a concise read-me so I can replicate or adapt every step. • One brief Q&A session or screen-share (recording is fine) where you guide me through the codebase and the data-science techniques you applied. If you enjoy tackling physics problems with Python and can teach as you code, this should be a quick win for both of us.