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 Aims
The module will cover basic maths and programming skills that you will require to progress on the MSc in Policy Analytics. The main aims of the module are:
- Develop basic maths skills for data analysis
- Develop proficiency in the use of relevant computer packages/languages (R, Python);
- Introduce you to Application Programming Interfaces (APIs) of various web sources (such as Twitter) to obtain large amounts of data allowing understanding of the scope of possibilities that are open to a researcher without special “big data” resources.
- Develop skills in managing large scale structured and unstructured data and constructing new databases from different sources;
- Develop skills in using R for data analysis.
- Develop skills in using R for data analysis.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 1. Demonstrate proficiency in the use of specific programming languages/packages used for statistical analysis: e.g. R and Python. 2. Understand code in R and implement appropriate commands to perform relevant statistical analyses (topics covered will include types of variables, functions and parameters, conditional commands and constructs such as while and for cycles). 3. Use Application Programming Interfaces to obtain data for potential use in future research projects (Python). |
| Discipline-Specific Skills | 4. Developed computer programming skills in a way that results in high level of synergies with quantitative research skills. 5. Manipulate data in each program and use the appropriate in-built analytic tools. 6. Interpret output from each program and draw appropriate inference regarding the hypotheses being tested. |
| Personal and Key Skills | 7. Demonstrate understanding of and use a full range of computing skills effectively and independently 8. Demonstrate understanding of and use a full range of data management skills effectively and independently |
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.
