Summer school

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 果冻影院 undergraduate and PhD students who want to gain new skills in social data science.听Our 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 Python听

This听course 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 of听data听wrangling听is to manipulate collected raw听data听so that it can be analysed for a specific research purpose. This course takes students through all the stages of听data听collection and manipulation including familiarity, structuring, cleaning and enriching using R's 'tidyverse' suite of packages. The course is very useful for anyone wanting to use听data听in their research, especially dissertations.

Survey Methods and Analysis

This听course听teaches the fundamentals of听survey听design and analysis, combined with practical applications using the statistical programme R. Specifically, it covers: sampling and weighting听methods听and how these relate to questions of representativeness;听survey听question and response scale design; different types of response bias, such as听social听desirability or non-response; effective ways to describe听survey听data; and a quick tour of the increasingly popular field of听survey听experiments. The guiding theme of the听course听is how to think critically about what information can (and cannot) be learnt from any specific听survey听data.

Introduction to Regression Analysis Using R

This听course听is designed for PhD students听with little or no prior background in quantitative听methods. It introduces participants to听data听analysis up to and including multiple regression using the R programming language. It combines lectures, seminars, and live coding sessions to cover the basics of manipulating听data, 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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