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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 UCL undergraduate and PhD students who want to gain new skills in social data science. 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
This 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 data wrangling is to manipulate collected raw data so that it can be analysed for a specific research purpose. This course takes students through all the stages of data 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 data in their research, especially dissertations.
Survey Methods and Analysis
This course teaches the fundamentals of survey design and analysis, combined with practical applications using the statistical programme R. Specifically, it covers: sampling and weighting methods and how these relate to questions of representativeness; survey question and response scale design; different types of response bias, such as social desirability or non-response; effective ways to describe survey data; and a quick tour of the increasingly popular field of survey experiments. The guiding theme of the course is how to think critically about what information can (and cannot) be learnt from any specific survey data.
Introduction to Regression Analysis Using R
This course is designed for PhD students with little or no prior background in quantitative methods. It introduces participants to data 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 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.