Python: For the Digital Humanities

Digital Humanities lies at the intersection of traditional Humanities research (close reading) and Computational Methodologies (distant reading). It is an interdisciplinary field currently expanding and overlapping with neighboring areas such as Computational Social Science or Digital Journalism. In this course we will explore how Digital Humanities uses a wide range of computational methodologies with Python that allow users to perform tasks such as data acquisition (webscraping), data analysis (cleaning and pre-processing, POS tagging, NER),  data storage (learning how to save our data (raw and processed) in CSV and txt files), data visualization (Geospatial Analysis), and Network Analysis.

In this introductory course, students will explore the basics of text analytics applied to Digital Humanities using several Python libraries such the Natural Language Toolkit (NLTK), Pandas, or BeautifulSoup (among others). Course content is disseminated over 12 hours through slides, live coding of the instructor and in-class exercises. We will use the Jupyter Notebooks interface provided by the Anaconda Environment.