Over the last six months I conducted a series of interviews for the LSE Impact Blog about the philosophical challenges which data science poses for the social sciences. Here's a list of the interviews: Rob Kitchin Evelyn Ruppert Deborah Lupton Susan Halford Noortje Marres Sabina Leonelli Emma Uprichard Here are some of my favourite bits…
We data scientists love to create exciting data visualizations and insightful statistical models. However, before we get to that point, usually much effort goes into obtaining, scrubbing, and exploring the required data. The command line, although invented decades ago, is an amazing environment for performing such data science tasks. By combining small, yet powerful, command-line tools you can quickly explore your data and hack together prototypes. New tools such as parallel, jq, and csvkit allow you to use the command line for today's data challenges. Even if you're already comfortable processing data with, say, R or Python, being able to also leverage the power of the command line can make you a more productive and efficient data scientist.
M. de la Iglesia. document | archive | disseminate graffiti-scapes. Proceedings of the goINDIGO 2022 International Graffiti Symposium, стр. 175-187. (2023)
A. Sato. Applications of Data-Centric Science to Social Design: Qualitative and Quantitative Understanding of Collective Human Behavior, Springer Singapore, Singapore, (2019)