This is a compulsory module for all Arts and Sciences (BASc) programmes.

There are no restrictions on enrolment on this module. However, this module is not suitable for students who are already pursuing degrees in heavily quantitative subjects (e.g. mathematics, computer science, physics, anything with significant statistical content). It is aimed at interdisciplinary students who wish to add quantitative skills to their toolkit and is delivered so as to most benefit those students.

This module will introduce students to a variety of quantitative tools for exploring and analysing data, with a focus on the application of such methods to interdisciplinary work. Students will learn to explore and communicate quantitative ideas with confidence and will be introduced to basic concepts of computer programming.

Indicative Topics

The module will cover the following topics, which may be subject to variation depending on developments in academic research and the interests of the class:

• Approaching Quantitative Problems
• Communicating Quantitative Arguments
• Introduction to Analysing Data
• Statistical Toolkit (e.g. Linear Regression, Cluster Analysis, Hypothesis Testing)
• Introduction to Computer Programming
• Introduction to Game Theory
• Interpreting Statistics in Everyday Life

By the end of the module, you should be able to:

1. Tackle quantitative problems with confidence;
2. Formulate high quality quantitative research questions;
3. Select, analyse and communicate the key features of data sets;
4. Understand and apply a variety of statistical techniques for data exploration;
5. Understand and apply basic programming concepts (e.g. loops, if statements, functions);
6. Think critically about statistics encountered in everyday life.

Teaching Delivery

The module is made up of 20 one-hour lectures and 10 one-hour seminars. Seminars are divided between interactive activity sessions, computer coding workshops and oral presentation sessions. Additionally, students will prepare individual research projects, with supervisory guidance from seminar leaders. Additional learning materials will be provided on Moodle to support lecture and seminar content, with occasional assignments and readings set, for discussion in class.

Recommended Reading

There is no required reading in advance of this module. However, students may wish to read more on some of the topics covered in the lectures in the following books:

• Munroe, R. (2014) 'What If?: Serious Scientific Answers to Absurd Hypothetical Questions'
• Silver, N. (2012) 'The Signal and the Noise: Why Most Predictions Fail – but Some Don't'
• Fry, H. (2018) 'Hello World: How to be Human in the Age of the Machine'
• Axelrod, R. (1990) 'The Evolution of Co-Operation'
• Downey, A. B. (2nd ed. 2015) 'Think Python: How to Think Like a Computer Scientist' [free online]

For further information on this module, please contact the named individual below.