Module SSIM905 for 2020/1
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SSIM905: Mathematics and Programming Skills for Social Scientists
This module descriptor refers to the 2020/1 academic year.
Module Aims
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.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 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. |
| Discipline-Specific Skills | 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. |
| Personal and Key Skills | 7. Demonstrate understanding of computing skills 8. Demonstrate understanding of data management skills |
Module Content
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 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 |
|---|---|---|
| 25 | 125 | 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 | 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 |
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 |
|---|---|---|---|
| 3 x Problem set of questions (one after each full day session) | 3 short exercises/questions each | 1-7 | Oral |
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 |
|---|---|---|---|---|
| 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 |
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 |
|---|---|---|---|
| Research project | Research project | 1-8 | August/September reassessment period |
