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Unit information: Mathematics and Programming Skills for Social Scientists in 2018/19

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Mathematics and Programming Skills for Social Scientists
Unit code GEOGM0032
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. David Manley
Open unit status Not open



Other AQM mandatory units

School/department School of Geographical Sciences
Faculty Faculty of Science


This is an advanced-level mathematics and programming course which aims to provide students with the essential mathematical skills needed to solve various types of optimisation problems and to introduce them to software with which they can solve practical optimisation problems within research.

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.

Intended learning outcomes

  • Acquisition of skills in specific data analysis methods and tools (for example, multi-level modelling);
  • Proficiency in the use of relevant computer packages/languages (MLwiN, R, Python);
  • Proficiency in using data from large scale surveys;
  • Ability to be able to manipulate and construct new data sets from secondary data sources;
  • Ability to select the appropriate analytical technique and associated computer program (and language) for the analysis required for a given research question.
  • Ability to use 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.
  • Proficiency in the use of three specific programming languages/packages used for statistical analysis: R, Python and MLwiN.
  • Ability to 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)
  • Develop coding skills in a way that results in high level of synergies with quantitative research skills.
  • Ability to manipulate data in each program and use the appropriate in-built analytic tools.
  • Ability to interpret output from each program and draw appropriate inference regarding the hypotheses being tested.
  • Ability to use APIs to obtain data for potential use in future research projects

Teaching details

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.

Assessment Details

One 2000 - 3000 word research project using the skills/techniques developed in one of the programming languages/applications to investigate a research problem relevant to the student’s chosen discipline. This will assess all learning outcomes

Reading and References