Module POLM030 for 2021/2
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
POLM030: Mathematics and Programming Skills for Policy Analytics
This module descriptor refers to the 2021/2 academic year.
Module Content
Syllabus Plan
Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:
- Introduction to R
- Introduction to Python
- Data Analysis in R
- Linear Algebra
- Big data
- Probability and frequentist inference
- Multilevel modelling
- Bayesian inference
- Advanced data visualisation
- Web scraping
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
20 | 130 | 0 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
Category | Hours of study time | Description |
---|---|---|
Scheduled Learning and Teaching Activities | 20 | 10 x 2 hours of lectures and labs. These lectures cover the main concepts of the course. Sessions will sometimes include group and lab work. |
Guided Independent Study | 40 | A variety of independent study tasks directed by module leader. These tasks may include (with an indicative number of hours): Assigned readings |
Guided Independent Study | 60 | Preparation for and completion of practical assessments |
Guided Independent Study | 30 | Practicing techniques used in computer tutorials |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
Big Data and Social Science (Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane): https://textbook.coleridgeinitiative.org
Maths Refresher Course (Gary King) http://projects.iq.harvard.edu/prefresher
UK Data Services - https://www.ukdataservice.ac.uk
NCRM - http://www.ncrm.ac.uk
Indicative Reading List
This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.
Basic reading:
Cioffi-Revilla, C. (2013). Introduction to computational social science: principles and applications, London: Springer Science & Business Media.
Gill, J. (2006). Essential Mathematics for Political and Social Research, Cambridge: Cambridge University Press.
Gelman, A & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge: Cambridge University Press.
Kropko, J. (2015) Mathematics for Social Scientists. London: SAGE Publications.
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. New York: CRC Press.