DHOXSS 2025 - an introduction to Applied Data Analysis

Yuxin Yang was awarded a bursary to attend the Applied Data Analysis strand of the Digital Humanities Oxford Summer School in 2025. To join the mailing list and learn about the next summer school sign up here. Read about Yuxin's experience at the summer school here:

I am Yuxin Yang, a PhD student in the Institute of Regional Sciences, Karlsruhe Institute of Technology (KIT), Germany. I am grateful for this opportunity to participate in the digital humanities summer school. I was looking forward to it from the beginning of this year and now I admit it is very worthwhile to learn lots of techniques and encounter new friends here. I really appreciate the guidance from our convenors, Ellen and Paul. I saw significant progress and robust results with their help at the end. I also want to express my gratitude to Emma, who contacted me from February and kindly provided me with essential documents to apply for the UK visa, as well as other tools and materials we need for the courses. And thank you for the bursary funded by Leonor Barocca, letting me not worry about the financial burden.

In our strand, people come from different fields, such as literature, history, architecture, and geography. They also come from different countries and different age groups. Everyone is inclusive and open-minded. Do not worry about your ability and do not be shy to ask questions; Ellen and Paul will give one-to-one guidance. If participants have a qualified database, this strand – applied data analysis – will be the best to join. We learned the basic techniques in the first two days, such as data cleaning, data statistics, and plotting. There are three more courses that we can explore in depth: Geomapping, natural language processing, and network analysis. Geomapping can be applied to display data from maps, such as shifting country names to geographical coordinates. Natural language processing (NLP) aims at processing text data, such as clustering topics from abundant documents. Network analysis can help to construct the relations between different actors. Most participants achieved displaying their data on the map. One of them made data fusion with his team member’s data and completed network analysis. The picture below shows my products from NLP. Everyone received a rubber duck from Ellen and Paul.

According to my own experience, it was a very intensive and fruitful week. Although I have learned Python by myself, I still found this course more systematic in establishing the programming basis, and I learned a lot of things that I had overlooked. Paul emphasized the different data types, which was key to giving specific functions to process data. We followed the steps one by one and were very thorough. Although the program often reported errors, sometimes it took me 2 hours, but I was extremely happy when I solved them. My seatmate and I always helped each other to solve problems. In the last 3 days, I didn’t have specific data to process, so it took me a lot of time to access data. Ellen gave me a lot of tips and guidance on my own project. Ellen and Paul were very supportive of my research ideas. Despite being in different research topics, their experience in methodology has given me a lot of inspiration. From this experience, I have more confidence in independently processing my own data during my work and a more proactive approach towards my research.

In addition to learning, we have many opportunities to meet people from other fields during coffee breaks and evening activities. I particularly enjoyed the poster exhibition on Monday night; it was a great chance to connect for the first time and engage in meaningful exchanges. I highly recommend this summer school to anyone interested in digital humanities and looking to discover new methods for processing their research.