Module POLM030 for 2019/0
- 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 2019/0 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 the R package 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 when 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
Data management in R
Data Analysis in R
Linear Algebra
Sets and Functions
Probability
Optimisation
Web scraping and data retrieval with Python
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 |
|---|---|---|
| Lectures with lab | 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. |
| Independent study | 130 | A variety of independent study tasks directed by module leader. These tasks may include (with an indicative number of hours): Assigned readings (40 hours) Preparation for and completion of practical assessments (60 hours) Practicing techniques used in computer tutorials (30 hours) |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
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
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| 2 short practical exercises | Between 2-4 tables, graphs, etc. with short descriptions | 1-8 | Oral feedback to group. Some written feedback. |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Assessment 1. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 2. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 3. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline. | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
| Assessment 4. 750 word practical exercise using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline. | 25 | 750 words with tables, figures, charts from analysis | 1-8 | Written Feedback |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Assessment 1 | Assessment 1 | 1-8 | August/September reassessment period |
| Assessment 2 | Assessment 2 | 1-8 | August/September reassessment period |
| Assessment 3 | Assessment 3 | 1-8 | August/September reassessment period |
| Assessment 4 | Assessment 4 | 1-8 | August/September reassessment period |
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, Claudio. Introduction to computational social science: principles and applications. Springer Science & Business Media, 2013.
Gill, Jeff. 2006. Essential Mathematics for Political and Social Research. Cambridge, England: Cambridge University Press.
Bush, ROBERT R., et al. "Mathematics for social scientists." The American Mathematical Monthly 61.8 (1954): 550-561.
Kropko, Jonathan. Mathematics for Social Scientists. SAGE Publications, 2015.
