Natasha Jenman 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 Natasha's experience at the summer school here:
The Digital Humanities Oxford Summer School 2025 was a wonderful event, and I am very grateful for the scholarship that gave me the opportunity to attend. It was a chance to hear about other participants' projects, to learn new methods and tools - and to commiserate over the particular frustrations of wrangling historical sources in the digital realm. More importantly, it was a week of productive conversations, truly brilliant teaching, and enjoyable learning.
I attended the Applied Data Analysis strand, for which you must have experience of Python beforehand, so that the course can dive right into the good stuff. And dive right in we did. From the very first session, we covered substantial ground, and it was time to dust off the cobwebs from my neglected coding skills. There was a mixed range of ability and confidence, but somehow the incredible course convenors kept everyone going at their own pace, with regular check-in points. The 'teaching' sessions were demanding, but rewarding, and as the convenors had promised (but I hadn't quite believed), things became progressively easier.
We were encouraged to provide our own datasets/projects to work on through the week. We set objectives, discussed the feasibility with the instructors, and hatched a gameplan to produce some results by the end of the week. My own research database looked a little too sparse to provide much practice, but I thankfully had another project that proved a viable stand-in. My goal was to 'clean' the spreadsheet and then create a diachronic map of some of the datapoints, using geopandas. My previous experience with mapping was all achieved through GIS, but this was precisely the purpose of the summer school: to learn the potential of new tools/methods and perhaps decide to integrate these into my DPhil work. For those who did not have practice data, the convenors were happy to provide alternative samples.
By the end of the week, all participants were so much more comfortable with the fundamentals and left with a new appreciation of how python and pandas can be utilised for their own research. In some sessions, time limitations meant we were offered a snapshot of a particular methods. For example, a comprehensive tutorial on natural language processing - in just one hour - would be ludicrous by any standards. However, the convenors made sure to thoroughly introduce each concept and offered tools and resources as a "next step" if anything sparked our interest.
On the final afternoon, everyone presented the work they had done on their own projects. It was wonderful to see how far we had come, and the new forms of data visualisation and analysis that we had unlocked. Our course convenors then awarded each of us with a colourful rubber duck for our desks - they had explained the "rubber duck theory" in coding, that when you need to debug your code sometimes explaining the issue to someone else (even an inanimate object) can help. As we left the week perhaps more confident in our claim to being "coders", the ducks will serve us well in the face of the inevitable "error" messages that appear; a sign to push on, because the end results are worth it.
