Setup
Before You Attend
Methods for Data Science is a foundation course for learners who collect, organise, interpret, or communicate data. You do not need programming experience, but you will get more from the course if you arrive with one real data example in mind.
Core Access Checklist
Before the first session, make sure you can:
- Sign in to your university account without difficulty.
- Open Microsoft Excel or another spreadsheet tool.
- Download and open example spreadsheets from the course site.
- Open the browser-based activities linked from the lessons; check any Microsoft Forms links supplied by the teaching team.
- Check joining instructions for the room or online session and any communication channel supplied by the teaching team.
Device and Materials
Bring:
- a laptop or tablet with a modern web browser
- a charger
- spreadsheet access, ideally Excel
- one public or fictional dataset, chart, dashboard, report, or data collection process you have used or questioned
No Python, R, or specialist statistics package is required. If spreadsheet or browser access fails, work with a partner or annotate the fictional practice table on paper. Record the choices and checks so you can repeat them later.
Practice Files
The lessons use an original student spreadsheet and a prepared student spreadsheet. Save a working copy before editing. The prepared file supports later exercises; it does not replace the need to explain cleaning choices.
For independent practice, use the Additional Material. Its fictional request dataset includes source notes, validation checks, and worked answers. You do not need to bring your own data to participate.
Session Preparation
| Session | Prepare before attending |
|---|---|
| Collecting the Right Data | Think of one data source you trust and one you would treat cautiously |
| Organising and Exploring Data | Bring a messy spreadsheet, table, or repeated reporting problem in mind |
| Making Sense of Data | Bring an example chart or dashboard you have used or questioned |
| Insight to Impact | Think of one analysis result that did or did not lead to action |
Data Safety
Use the supplied practice files or a public or fictional example for room activities. If you want to discuss a real project, describe its question and structure without sharing personal, confidential, or unpublished material. Removing names alone may still leave people identifiable.