Module 1
Thinking Like a Scientific Programmer
Learn Python from zero and become capable of inspecting, organising and explaining scientific ecological data.
- Portfolio project
- Vegetation Data Explorer
- Prerequisites
- None
Learning outcomes
- Use Jupyter and Python confidently
- Represent scientific information using appropriate data types
- Write conditions, loops and functions
- Work with NumPy arrays
- Load and inspect a published dataset with pandas
- Identify missing, invalid and inconsistent values
- Filter, group, summarise, join and reshape tables
- Create clear scientific figures
- Document and interpret a reproducible analysis
Lessons and portfolio activities
- 1.1Welcome to Scientific ProgrammingCore · 60–75 minutes
- 1.2Variables and Scientific DataLesson · 70–85 minutes
- 1.3Collections for Ecological InformationLesson · 90–105 minutes
- 1.4Conditions and Data-Quality RulesLesson · 90–105 minutes
- 1.5Repetition, Loops and Vectorised ThinkingLesson · 90–105 minutes
- 1.6Functions, Errors and DebuggingLesson · 95–115 minutes
- 1.7NumPy and Numerical ArraysLesson · 95–115 minutes
- 1.8Open the Published Dataset with pandasLesson · 105–125 minutes
- 1.9Missing Values, Types and Data QualityLesson · 105–130 minutes
- 1.10Filter, Group and SummariseLesson · 105–125 minutes
- 1.11Join, Reshape and VisualiseLesson · 115–140 minutes
- 1.12Vegetation Data Explorer ProjectLesson · 3–5 hours