# Overview

This section is about **data** — the formats and conventions you'll meet when working with climate and environmental datasets, and the practical ways to get data into Python.

## Learning objectives

By the end of this section, you should be able to:

1. **Recognize common data formats** in climate science — tabular (CSV, Parquet), gridded (NetCDF, Zarr), and raster (GeoTIFF).
2. **Read metadata** and understand why it's essential to a dataset's usefulness.
3. **Explain the FAIR principles** (Findable, Accessible, Interoperable, Reusable) and why they matter for scientific data.
4. **Access data from different sources** — over HTTP, via OPeNDAP, and via persistent identifiers (DOIs) with Pooch.
5. **Find a DOI** for a dataset and use it to retrieve the data reproducibly.

## Pages in this section

- [Formats and metadata](./formats_and_metadata.md) — what kinds of data exist, how they're described, and what FAIR means.
- [Loading data](./loading_data.md) — practical try-its for the main access patterns.
- [Assignment 2](./assignment2.md) — explore and document a climate dataset of your choice.