# Skills to Build During Your Undergraduate Degree

Ranked by market impact. The redesigned curriculum covers all 11 skills below.

```{admonition} Reading the dots
:class: note
Dot ratings: ●●●●● essential by graduation · ●●● differentiating · ●● graduate-level bonus
```

## Technical skills — priority & depth

| Skill | Priority |
|-------|----------|
| Python for scientific computing | ●●●●● |
| Seismic methods (reflection, refraction, surface wave) | ●●●●● |
| Signal processing (Fourier, filtering, spectral analysis) | ●●●●○ |
| Earth interior & seismology fundamentals (PREM, phases) | ●●●●○ |
| Gravity & magnetics (forward modeling, corrections) | ●●●●○ |
| Git / version control + reproducible environments | ●●●●○ |
| Technical writing (IMRaD, methods, results, interpretation) | ●●●●○ |
| ML applied to geo data (scikit-learn, PyTorch) | ●●●○○ |
| Inverse problems & regularization theory | ●●●○○ |
| AI tool fluency — prompt engineering, rubric design, critical evaluation | ●●●○○ |
| FAIR data practices + data provenance | ●●○○○ |

## What your intro geophysics course specifically gives you

ESS 314 covers:

- **Seismic wave theory** — P, S, surface waves; travel-time curves; ray tracing
- **Earth structure** — crust, mantle, core; PREM; teleseismic phases
- **Gravity anomalies** — isostasy; Bouguer and free-air corrections
- **Magnetic surveying** — total-field anomaly; susceptibility; forward models
- **Forward modeling intuition** — every method as a physics problem

When combined with the Python notebooks, you convert conceptual knowledge into demonstrable computational skills that show up directly on employer skill screens.

## Portfolio-building actions (high ROI)

The actions below are low-cost during the course and high-value afterward:

1. **Public GitHub** with 3–5 well-documented geophysics notebooks, built incrementally across 10 weeks
2. **One short technical blog post** about something you computed (Medium, Substack, or personal site)
3. **Present** at AGU student section, EarthScope workshop, or UW departmental seminar
4. **One REU, internship**, or volunteer research semester in a UW ESS lab
5. **A documented AI agent system prompt** in your GitHub repo (`agent_instructions/report_reviewer_v1.md`) — a concrete, rare portfolio artifact in 2026
6. **A clean `environment.yml`** with pinned package versions so your notebooks pass a fresh-clone test

```{note}
A candidate who walks into an interview with a GitHub portfolio containing geophysics notebooks, a documented AI reviewer agent, and a reproducible environment file is concretely differentiated from a candidate who only has coursework on a résumé.
```