Find out more about the Social Data Institute's summer school courses.
Each year we run a series of free short courses in the summer term, lasting 2-5 days, for °×С½ãÂÛ̳ undergraduate and PhD students who want to gain new skills in social data science.ÌýOur summer school courses will be advertised to students by departments. The selection may change each year; below we offer a selection of the courses that we have offered recently.
Foundations of PythonÌý
ThisÌýcourse introduces programming in python including program design, version control with git, and data science tools in python. It is very useful for anyone hoping to use python in a research or work setting and culminates in a collaborative data science challenge using Python.
Data Wrangling in R
The aim ofÌýdataÌýwranglingÌýis to manipulate collected rawÌýdataÌýso that it can be analysed for a specific research purpose. This course takes students through all the stages ofÌýdataÌýcollection and manipulation including familiarity, structuring, cleaning and enriching using R's 'tidyverse' suite of packages. The course is very useful for anyone wanting to useÌýdataÌýin their research, especially dissertations.
Survey Methods and Analysis
ThisÌýcourseÌýteaches the fundamentals ofÌýsurveyÌýdesign and analysis, combined with practical applications using the statistical programme R. Specifically, it covers: sampling and weightingÌýmethodsÌýand how these relate to questions of representativeness;ÌýsurveyÌýquestion and response scale design; different types of response bias, such asÌýsocialÌýdesirability or non-response; effective ways to describeÌýsurveyÌýdata; and a quick tour of the increasingly popular field ofÌýsurveyÌýexperiments. The guiding theme of theÌýcourseÌýis how to think critically about what information can (and cannot) be learnt from any specificÌýsurveyÌýdata.
Introduction to Regression Analysis Using R
ThisÌýcourseÌýis designed for PhD studentsÌýwith little or no prior background in quantitativeÌýmethods. It introduces participants toÌýdataÌýanalysis up to and including multiple regression using the R programming language. It combines lectures, seminars, and live coding sessions to cover the basics of manipulatingÌýdata, quantitative research designs and regression analysis. The aim is to equip students with the skills needed to apply quantitative research techniques in their own research projects.
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