Mathematics and Programming Skills for Social Scientists
| Module title | Mathematics and Programming Skills for Social Scientists |
|---|---|
| Module code | SSIM905 |
| Academic year | 2021/2 |
| Credits | 15 |
| Module staff | Dr Lorien Jasny (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 6 |
|---|
Module description
You will cover the maths and programming skills needed to progress through the MRes in AQM. You will be a social science student with understanding of and skills in quantitative methods. This mathematics and programming course will aim to provide you with the essential mathematical skills needed to solve various types of optimisation problems and to introduce them to software with which you can solve practical optimisation problems within research. You will need a sufficient level of quantitative methods training and a willingness to engage with new material.
Module aims - intentions of the module
The module aims to introduce you to basic maths and programming skills that you require to progress on the MRes in AQM. The topics covered include:
- Basic maths skills for data analysis
- Proficiency in the use of relevant computer packages/languages (MLwiN, R, Python);
- Managing data from large scale surveys and constructing new databases from different sources;
- Ability to be able to manipulate and construct new data sets from secondary data sources;
- 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.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate proficiency in the use of three specific programming languages/packages used for statistical analysis: R, Python and MLwiN.
- 2. Understand code in each language 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 APIs to obtain data for potential use in future research projects.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Developed coding skills suitable for conducting high-level complex quantitative analyses.
- 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.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Demonstrate understanding of computing skills
- 8. Demonstrate understanding of data management skills
Syllabus plan
This course is delivered via three full-day sessions, one in each institution (Bath, Bristol and Exeter) plus pre-reading delivered online in advance of each full-day session. Additional computer lab sessions also take place within ‘home’ institutions to prepare the coursework. The main topics covered are programming statistical and graphical techniques using R; dynamic programming and coding using Python; multi-level modelling theory and application using MLwiN. Each day-long session will involve lectures outlining the theory behind a technique or the rudiments of a programming language, its application and use, along with practical sessions implementing the skills learned on a common dataset that will be used for each of the three day-long sessions and with each of the different computing packages.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 25 | 125 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 9 | 2 x 1.5 hour lectures in each full-day session These lectures cover the main concepts of the course. |
| Scheduled Learning and Teaching Activities | 12 | 2 x 2 hour practical classes in the computer labin each full-day session: These practical sessions cover the application of techniques |
| Scheduled Learning and Teaching activities | 4 | 2 x 2 hour additional practical classes in home institution outside of co-taught full-days. |
| Guided independent study | 30 | Reading the relevant literature discussed in class |
| Guided independent study | 50 | Reading and preparing materials for the research project that constitutes the modules summative assessment |
| Guided Independent Study | 45 | Acquiring additional experience with software and computing tools required to conduct the type of analyses discussed in class |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| 3 x Problem set of questions (one after each full day session) | 3 short exercises/questions each | 1-7 | Oral |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| One 2750 word research project using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the students chosen discipline. | 100 | 2750 words | 1-8 | Written Feedback |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Research project | Research project | 1-8 | August/September reassessment period |
Indicative learning resources - Basic reading
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, Eng- land: Cambridge University Press.
Indicative learning resources - Web based and electronic resources
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
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 28/11/2016 |
