Overview

A convincing result is stronger when another analyst can reproduce it from the documented inputs. This course introduces reproducibility as a normal part of analytical practice rather than an afterthought.

Learners organize raw data, transformations, code, model assumptions, figures and notes into a transparent workflow. Simple versioning and sensitivity checks are used to show how results change when decisions change.

The course focuses on principles that transfer across Python, R, spreadsheets and other tools rather than prescribing one software stack.

Learning objectives

By the end of the course, learners should be able to:

• separate raw data from transformed and derived data;
• document analytical assumptions and processing steps;
• organize scripts or notebooks into a repeatable workflow;
• produce figures and tables directly from recorded analysis;
• use versioning and sensitivity checks to diagnose changes;
• prepare a small analytical package another person can rerun.

Intended participants and prerequisites

Course structure

The course is organised into six units. Each unit combines concise explanation, worked examples and short analytical exercises.

Unit	Subject	Principal questions
1	Inputs and provenance	Where did each dataset come from, and what has been changed?
2	Transformations	How should cleaning and feature construction be documented?
3	Analysis code	How can calculations be organized so that they can be rerun?
4	Outputs	How can tables and figures remain linked to the analysis that produced them?
5	Sensitivity and versions	How should alternative assumptions and revisions be tracked?
6	Replication package	What should another analyst receive to reproduce the result?

Teaching and assessment

Teaching is delivered through concise lectures, technical notes, worked examples and guided exercises. AI-supported interaction may be used to explain concepts, generate additional practice and challenge a learner's reasoning, but assessment remains based on the learner's demonstrated understanding.

Component	Purpose	Weight
Short exercises	Check understanding of the principal concepts	40%
Applied investigation	Use the course framework on a new problem or dataset	30%
Final assessment	Integrated reasoning across the course	30%

A certificate of completion is issued only when the required assessments have been completed at the stated standard. Participation alone does not constitute completion.

Faculty

Materials and technical requirements

Learning environment

Learners need a current web browser and access to the Gordon learning platform. Where a course uses code, spreadsheets, datasets or external readings, the relevant requirements are identified inside the unit.

Course materials

Required readings, datasets and practice materials are identified within each unit. Public courses are designed so that required core materials can be accessed without a separate tuition charge.