Dendrology · Learning Paths

Learning Paths

A learning path is an ordered route through the archive: existing entries sequenced toward a skill, with checkpoints that turn reading into practice. Each path shows exactly how much of it exists today — entries still to be written are listed as the gaps they are, and they double as the archive's writing backlog.

Every path is free and fully readable without an account. Signing in adds memory, not walls: enrollment and per-step checkmarks, nothing more.

Environmental Data Scientist

From "what is an algorithm" to a working, evaluated weather dataset: the machine-learning wing's entries sequenced into one route, with checkpoints that turn reading into practice.

Who it's for: Readers starting from little or no machine-learning background who want to work with environmental data — no prerequisites beyond curiosity and a computer that runs Python.

8 of 10 steps available today — 5 published entries + 3 checkpoints; 2 entries still planned

Open the path →

Precision Agriculture Specialist

Environmental systems, irrigation, and harvest timing stitched into one route toward data-driven farming — ending with the field-data skills that turn weather feeds into planting-season decisions.

Who it's for: Growers, agronomy students, and career changers who want to bring data into field decisions — comfortable reading charts, no programming background assumed until the dataset steps.

8 of 10 steps available today — 5 published entries + 3 checkpoints; 2 entries still planned

Open the path →

Coverage counts are computed from the archive at build time — they change only when a referenced entry is actually published.