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XX50217: Mathematics and programming skills for social scientists

Follow this link for further information on academic years Academic Year: 2019/0
Further information on owning departmentsOwning Department/School: Faculty of Humanities & Social Sciences (units for MRes programmes)
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • XX500217 research project (CW 100% - Qualifying Mark: 40)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Further information on descriptions Description: Aims:
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.

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.

Skills:

* 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.

Content:
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.
Further information on programme availabilityProgramme availability:

XX50217 is a Designated Essential Unit on the following programmes:

Department of Social & Policy Sciences
  • THXX-AFM54 : MRes Advanced Quantitative Methods in Social Sciences
  • THXX-AFM76 : MRes Advanced Quantitative Methods in Social Sciences (Leading to PhD)
  • THXX-APM54 : MRes Advanced Quantitative Methods in Social Sciences

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