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Unit information: Mathematics and Programming Skills for Social Scientists in 2020/21

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 Mr. Hayes
Open unit status Not open
Pre-requisites

As this is an advanced-level course, working knowledge of the theory and application of regression analysis is assumed; experience of rudimentary programming is preferable.

Co-requisites

Other AQM mandatory units

School/department School of Geographical Sciences
Faculty Faculty of Science

Description

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 syntax-driven logistic regression in SPSS leading to multi-level modelling theory and application using MLwiN; 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

Upon successful completion of this unit, students will have:

  1. Acquisition of skills in specific data analysis methods and tools (for example, multi-level modelling);
  2. Proficiency in the use of relevant computer packages/languages (SPSS, MLwiN, R, Python);
  3. Proficiency in using data from large scale surveys;
  4. Ability to be able to manipulate and construct new data sets from secondary data sources;
  5. Ability to select the appropriate analytical technique and associated computer program (and language) for the analysis required for a given research question.
  6. 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.
  7. 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)
  8. Developed coding skills in a way that results in high level of synergies with quantitative research skills.
  9. Ability to manipulate data in each program and use the appropriate in-built analytic tools.
  10. Ability to interpret output from each program and draw appropriate inference regarding the hypotheses being tested.
  11. Ability to use APIs to obtain data for potential use in future research projects

Teaching details

This course is delivered in sections by each institution (Bath, Bristol and Exeter)

Assessment Details

One 2000-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. (Exact word limit is dependent on the insitution delivering the assessment.)

Reading and References

To be advised by tutor if applicable.

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