Data and Text Analysis Summer School

Lighthouse over old etching of Leith docks

The Data and Text Analysis Summer School will provide the attendees with an agile and customisable training platform that will guide them through different aspects of using Python and R for data and text analysis.

The course will start with a basic introduction to data wrangling and will progress into the more complex topics of data visualisation, text analysis, geographical data analysis, and networking analysis.

The training is organised in blocks. Attendees can sign up for one or more blocks depending on their interests. Two coding languages (R and Python) will be taught, and attendees may choose to attend all blocks or only the blocks focused on their language of choice. 

Depending on background/interests, attendees will be able to choose one or more tailored hands-on classes that will give them the tools and basic knowledge required to develop their knowledge further.

There are no prior knowledge entry requirements; however, the basics on the interfaces of Python and R will be covered in Day 1 (Data Wrangling). It is, therefore, not recommended to absolute beginners to sign up to the following days' classes without having attended Day 1 or without having familiarised themselves with the coding interface. 

Timetable for the Summer School

Attendee Sign up

This summer school is primarily targeted at researchers from higher education institutions from Edinburgh City and its regions, with a marked interest in developing their digital and computational skills. The course is also open to professionals based in Scotland interested in upskilling their digital methods and implementing good digital practices in their everyday work. 

This event is free to attendees but places are limited. 

Candidates should submit an application detailing which blocks they hope to attend and how attendance will benefit their research/work. 

The Expression of Interest form is now closed.


  • 12 April - Expression of Interest form opens
  • 14 May - Expression of Interest form closes
  • 21 May - Offers sent out
  • 07 June - Summer School starts 
logo data-Driven innovation

This initiative is funded by the Data-Driven innovation Initiative as part of their ‘Building Back Better’ open funding programme, helping to transform the City region into the data capital of Europe. It is supported by the Scottish Funding Council Covid-19 Recovery funding to the University of Edinburgh. The Scottish Funding Council has provided £75m funding to boost Scottish university research and to contribute to the mitigation of effects of the Covid-19 pandemic. The University of Edinburgh received £23.2m of these funds.

One of the goals set by Scottish Funding Council is to fund translation of research into action, to support our region’s recovery. The University of Edinburgh has allocated £4m fund for this purpose, to be coordinated as part of the Data-Driven Innovation initiative. Through these activities, they aim to deliver impact for communities, services and businesses.

Instructors and Helpers

Justin Chun-Ting Ho

Justin Chun-ting Ho

Justin Chun-ting Ho is a postdoctoral researcher at Sciences Po.

He holds a PhD in Sociology from the University of Edinburgh. His research focuses on nationalism and populism with a focus on how they are communicated via social media.

His research employs a range of computational methods, including computational text analysis and social network analysis.


Andrew McLean

Andrew Mclean

Andrew McLean is based in the School of History, Classics and Archaeology.

Andrew is an archaeologist with research interests currently focused on the economy of the Roman Adriatic. His methodological approaches include GIS and statistical analysis, particularly expanding on traditional Least Cost Path (LCP) analysis by using circuit theory to model maritime movement. Through this, he is familiar with QGIS, R, Circuitscape, shell scripting and programming languages such as Julia and Python.


Esgrid Sikahall Urizar

Esgrid Sikahall Urizar

Esgrid Sikahall Urizar has worked as a Mathematics Lecturer in Guatemala (including Numerical Analysis) before moving to the UK to study Philosophy, Religion and Science. He has worked on Python, Matlab and as a web programmer using HTML, PHP, CSS, and MySQL. His current research is on philosophical hermeneutics (Hans-Georg Gadamer) and the implementation of historiographical works on science and religion in wider cultural spaces and discourses.


Pu Yan

Pu Yan

Dr Pu Yan is a researcher at Oxford Internet Institute. Her research interests include online information behaviour, digital divides, and the use of digital media in everyday life. She is currently working on the project “What Do ‘the People’ Want? “, which combines traditional social science approaches and computational social science methods to understand the use of digital media and the rise of populism in six countries. Before joining the Oxford Internet Institute, she obtained an undergraduate degree (Honour) in Journalism and Communication from Tsinghua University in 2014, and a master’s degree (Distinction) in Social Science of the Internet from Oxford Internet Institute in 2015. 

Her research interests are information-seeking practices; digital media; computational social science; Chinese Internet; digital divides


Robert Nagy

Robert Nagy

Robert is a final-year Health Data Science PhD student at the University of Edinburgh Cancer Research UK Centre, a teaching assistant at the University of Edinburgh Business School, and a student member of the Edinburgh Clinical Trials Unit’s (ECTU) Edinburgh Health Economics (EHE) Academic Group. Before starting the PhD, Robert gathered skills from highly multidisciplinary (B.Sc & M.Sc) programmes spanning across Molecular Bionics, Biomedical Engineering, Business, and Data Science, which are complemented by previous academic research in the fields of Neuroscience and Biophysics. Robert's PhD research aims to find better ways for estimating the affordability of novel breast cancer medicines by making the current budget impact analysis tool real world- and evidence-based. Robert uses R and RStudio for statistical computing, and is a certified Data Carpentry instructor.



Xing Chen

Xing Chen

Xing Chen is a PhD student at the University of Edinburgh Business School (UEBS). Prior to this, he obtained a master's degree in Marketing and Business Analysis from UEBS and accumulated great work experience in digital marketing and data analysis from leading internet and advertising companies.

His current research interests lie in the adoption and diffusion of emerging technologies among organisations, with a specific focus on the socio-technical perspectives of blockchain application in carbon markets.


Luis Reyes

Luis Alberto Reyes Figueroa

Luis is a PhD candidate at the School of Law, University of Edinburgh. His research mainly draws on SEM multilevel analysis with categorical data, using Mplus. He is also a tutor for quantitative analysis courses at the School of Social and Political Sciences, where SPSS and Stata are used in practical labs. He has also collaborated with Data Carpentry and the Edinburgh Center for Data, Culture and Society on statistical analysis and visualization courses with software such as R, SQL and Open Refine.