CASA0007: Quantitative Methods
Module Overview
As data becomes central to decision-making across all sectors, the ability to move beyond simple description to robust and evidence-based analysis is essential for researchers and practitioners in geography and urban studies. Making sense of the complex patterns that define our world requires a firm understanding of the language of data. This module provides the essential grammar for that language and aims to equip students with the foundational quantitative skills needed to describe data, build models, and derive meaningful insights from numerical evidence.
To achieve this, the module is structured progressively across three core sections. The first section establishes the fundamental toolkit for quantitative analysts. Beginning with Exploratory Data Analysis, students will learn how to visualise, summarise, and critically interrogate datasets. This is followed by a rigorous grounding in the principles of statistical inference through hypothesis testing, and an introduction to the linear algebra that provides the mathematical architecture for many of the models used in modern data science.
The second section builds upon this foundation to explore the core of statistical modelling: understanding and quantifying relationships between variables. Students will progress from measuring correlations to building sophisticated regression models. The curriculum covers the workhorse of social science, the Generalised Linear Model (GLM), before advancing to Multilevel Models, a critical technique for handling the nested and hierarchical data that is common in geographical and social research.
The final section of the course introduces advanced techniques for uncovering hidden structures within complex, high-dimensional datasets. Students will learn methods for dimensionality reduction to simplify complexity without losing vital information, and clustering analysis to identify natural groupings and patterns in data.
Therefore, this module guides students on a complete analytical journey, from foundational principles to the application of advanced modelling techniques. It serves as a vital prerequisite for more specialised analytical modules and is essential for students wishing to undertake quantitative geospatial research. Ultimately, this module will provide quantitative skills that are in high demand across public, private, and academic sectors.
Table of Contents
- Basics
- Four initial weeks on probability, data types, hypothesis testing, linear algebra.
- Correlation and Regression
- Four weeks on correlation and regression.
- Dimension Reduction & Clustering
- Two weeks on techniques of dimension reduction and clustering.