Workshop Program Hub
Select a day tab and jump directly to the exact course part you want. Each module has direct access buttons for Presentation and Practical where available.
Day 1 Program
(1) R Kickstart and Data Import
Environment setup, first script flow, and importing experimental datasets with reliable structure.
(2) Core Data Structures and Summaries
Work confidently with vectors, factors, data frames, and summary workflows for quick diagnostics.
(3) Experimental Data Quality and Error Checks
Detect anomalies early, validate assumptions, and standardize data-quality checks for analysis readiness.
(4) Data Cleaning and Variable Management
Clean, recode, and normalize variables to keep downstream models consistent and reproducible.
Day 2 Program
(5) Feature Engineering and Data Joins
Create robust derived variables and combine datasets safely while preserving scientific intent.
(6) Data Reshaping and Consistency Checks
Move between long and wide structures and enforce consistency checks before statistical tests.
(7) Data Exploration Foundations
Use fast exploratory loops to identify signal, structure, and plausible biological interpretation.
(8) Assumptions, Outliers, and Group Comparison
Stress-test assumptions, diagnose outliers, and compare groups with transparent decision logic.
(9) Publication-Ready Data Visualization
Design clean, accurate figures for papers and talks with reproducible plotting patterns.
Day 3 Program
(10) Communicating Treatment Effects with Figures
Transform complex outcomes into clear visual narratives that support evidence-based discussion.
(11) T-Tests and Statistical Interpretation
Build sound inference workflows and interpret test results with effect-size aware reporting.
(12) From Question to Experimental Blueprint
Convert research questions into testable study blueprints with practical implementation detail.
(13) Replication, Randomisation, and Design Rigor
Strengthen study reliability with robust design principles and transparent reproducibility standards.
(14) Ecological Sampling and Analysis Alignment
Align field sampling strategy with analysis plans so interpretation remains coherent end-to-end.