Undergraduate Module Descriptor

SSIM905: Mathematics and Programming Skills for Social Scientists

This module descriptor refers to the 2021/2 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.

Intended Learning Outcomes (ILOs)

This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills1. 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 Skills4. 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 Skills7. 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 ActivitiesGuided independent studyPlacement / study abroad
251250

...and this table provides a more detailed breakdown of the hours allocated to various study activities:

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activities92 x 1.5 hour lectures in each full-day session These lectures cover the main concepts of the course.
Scheduled Learning and Teaching Activities122 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 activities42 x 2 hour additional practical classes in home institution outside of co-taught full-days.
Guided independent study30Reading the relevant literature discussed in class
Guided independent study50Reading and preparing materials for the research project that constitutes the module’s summative assessment
Guided Independent Study45Acquiring 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