Overview
Why study a Quantitative Methods course?
- Coding alone is not enough.
- Understanding models aids in correct tool selection and bug handling.
- Google/Claude Code can make mistakes; detecting them is crucial.
Course Objectives
- Understand a broad range of quantitative techniques.
- Apply these skills in research.
- Formulate coherent quantitative argument.
Prerequisites
- No prerequisite of university-level maths/statistics.
- No prerequisite of programming.
- Python (or R) is required for practicals and assessments.
- This module doesn’t teach programming, so CASA0013 is strongly recommended if you don’t know any Python.
Course Structure
- Lectures: Tuesdays 9:00–10:30; G13 Torrington Place (1-19).
- Tutorials: Tuesdays 10:30–12:00; G13 Torrington Place (1-19).
Platforms
Weekly Schedule
| Session | Topic | Lecturer |
|---|---|---|
| 1 | Introduction to data | Adam |
| 2 | Probability and distribution | Bea |
| 3 | Hypothesis testing | Bea |
| 4 | Introduction to linear algebra | Bea |
| 5 | Correlation and regression | Adam |
| 6 | Intro to Regression | Adam |
| 7 | Multiple Regression | Adam |
| 8 | Multilevel regression | Adam |
| 9 | Dimensionality reduction | Bea |
| 10 | Clustering Analysis | Bea |
How will the module be assessed?
Assessment
- Written Investigation (summative): 100%
- Weekly quiz (formative)
UCL Assessment Policy
- All submissions via Moodle, not emails.
- Late penalties: Up to 2 working days (-10 points); up to 7 working days (capped at 50); over 7 working days (scores 0).
- DAP or Extenuating circumstances: to submit on Portico.
- Respect word count limits
- Avoid plagiarism and unverified references
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