Overview

Author

Huanfa Chen

Published

October 1, 2025

Why study a Quantitative Methods course?

  • Coding alone is not enough.
  • Understanding models aids in correct tool selection and bug handling.
  • Google/ChatGPT can make mistakes; detecting them is crucial.
  • Mathematics understanding is not always required.

Objectives

  1. Understand the structure and focus of the module.
  2. Develop a method for tackling quantitative problems.
  3. Formulate a research question and structure quantitative writing.

Course Objectives

  1. Understand a broad range of quantitative techniques.
  2. Apply these skills in research.
  3. Formulate a 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 recommneded if you don’t know Python before.

Course Structure

  • Lectures: Wednesdays 9:00–10:30; G13 Torrington Place (1-19).
  • Tutorials: Wednesdays 10:30–12:30; G13 Torrington Place (1-19).

Platforms

  • Email for important notices and private questions.
  • Github & website for lecture notes and notebooks.
  • Moodle for lectures recording and assessments.
  • Slack for public questions.

Weekly Schedule

Session Topic Lecturer
1 Introduction to data Huanfa
2 Probability and distribution Bea
3 Hypothesis testing Bea
4 Introduction to linear algebra Bea
5 Correlation and regression Huanfa
6 Multiple regression Adam
7 Generalised linear model Adam
8 Multilevel regression Adam
9 Dimensionality reduction Huanfa
10 Clustering Analysis Huanfa

Assessment

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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