L1 - Getting Started

In this first lesson, you set up the foundation for everything that follows in SDS210.

Before we dive into variables, loops, or functions, you need two things:

1. a clear reason for why we code in this course

2. a reliable way to run code and understand what happens

That is what Lesson 1 is for.

1. From questions to code¶

Spatial data science starts with questions:

• What changes over time

• Where are patterns stronger or weaker

• How do processes differ across space

Raw data rarely answers these questions directly. Code helps you turn messy inputs into readable results and reproducible workflows.

In this lesson, you move from motivation to method:

• why programming matters for data and science

• what it means to “program” in this course

• how Python code is structured

• how notebooks execute code step by step

2. Why this matters for SDS¶

In later lessons you will work with:

• tables of measurements

• coordinates and geometries

• raster and vector datasets

• automated workflows that must run reliably

All of that depends on basic habits you build now:

• writing code with clear structure

• running notebooks in a reproducible order

• understanding what a kernel remembers and what it does not

If these basics are solid, the rest of the course becomes much easier.

3. Learning objectives¶

After this lesson, you will be able to:

• Explain why programming supports data understanding and reproducibility
  Articulate how code transforms raw data into interpretable and repeatable results.

• Run and manage notebooks correctly
  Execute cells in the right order, restart the kernel, and ensure that a notebook works from top to bottom without hidden state.

• Recognise core structural elements of Python syntax
  Identify identifiers, comments, strings, and indentation, and understand why they matter for readable and valid code.

4. Lesson structure¶

This lesson has four short sections:

1. Motivation

2. Programming & Python

3. Python syntax

4. Running notebooks

Each section is designed to be quick to read and immediately usable.

5. Looking ahead¶

Lesson 1 is about getting started safely. In Lesson 2, you will build on this by learning how Python stores information using variables and values.

Once you can store data, you can start organising it, transforming it, and scaling your analysis.