Digital Humanities: From Research to Results
Learn to combine literary research with data science in this Harvard course on digital humanities practices.
Learn to combine literary research with data science in this Harvard course on digital humanities practices.
This course explores the intersection of humanities research and data science, focusing on how computational methods can enhance the study of literature, history, and philosophy. Participants will learn to use basic coding tools to analyze large digital document collections, deriving insights that were previously impossible. The curriculum covers text analysis techniques, metadata manipulation, and visualization methods, all applied to real-world humanities research questions. Students will build parts of a search engine tailored for academic research, learning fundamental text analysis skills applicable to various fields. The course emphasizes practical skills, including downloading datasets, web scraping, using APIs, and writing Python code. By the end, learners will be able to apply these digital methods to diverse materials, from 18th-century literature to contemporary speeches, journalism, and even art objects.
3.9
(14 ratings)
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What you'll learn
Understand which digital methods are suitable for analyzing large text databases
Identify resources and limitations for complex digital humanities projects
Download datasets and create new ones using web scraping and APIs
Enrich metadata and tag text to optimize analysis results
Apply digital methods like topic modeling and vector models to analyze thousands of books
Write and edit Python code for text analysis and search optimization
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to digital humanities methods, focusing on the practical application of data science techniques to humanities research. The curriculum covers a range of topics, including text analysis, metadata manipulation, web scraping, and data visualization. Students will learn to use Python for humanities research, building skills in coding and data manipulation. The course emphasizes hands-on learning, with projects that include building parts of a search engine and analyzing large text corpora. Participants will explore advanced concepts such as topic modeling and vector models, applying these techniques to real-world humanities datasets. Throughout the course, students will learn to identify appropriate digital methods for different research questions, understand the limitations and potential of these methods, and interpret the results of computational analyses in the context of humanities scholarship.
Fee Structure
Instructors
1 Course
Library Technology Expert Advancing Digital Metadata Standards
Christine Fernsebner Eslao, Metadata Technologies Program Manager for Harvard Library Information & Technical Services, has established herself as a leader in library metadata innovation and linked data implementation. After earning her BA in Liberal Arts from Hampshire College and MLIS from Simmons College Graduate School of Library & Information Science, she has built a career transforming library metadata workflows. Beginning as a copy cataloger, she now leads initiatives to modernize metadata management through linked data technologies and identity management systems. Through her role at Harvard Library, she works to improve discoverability of library resources while implementing next-generation metadata standards that enhance the accessibility and interoperability of digital collections. Her expertise in metadata technologies contributes to Harvard Library's broader digital strategy of developing networked resources and supporting digital scholarship across the university.
1 Course
Digital Humanities Engineer Bridging Technology and Literary Research
Cole Crawford, Senior Software Engineer in Harvard's Arts and Humanities Research Computing unit (DARTH), has established himself as a key figure in digital humanities infrastructure development. After earning degrees spanning both technology and literature - including an ALM in Software Engineering from Harvard Extension, MA in Literature and Culture from Oregon State University, and BS in Computing Science and English from Creighton University - he has built a career bridging technical expertise with humanities scholarship. As former staff co-chair of Harvard's Digital Scholarship Support Group and current steering committee member for DHTech, he develops digital infrastructures enabling scholars to model, analyze, and share their research. His work combines software engineering with literary expertise, particularly focusing on eighteenth and nineteenth-century British laboring-class writers. Through his roles on the Harvard IT Staff Council and active participation in the IIIF and ADHO communities, he continues to advance digital humanities while helping scholars leverage technology to reach broader audiences with their research.
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3.9 course rating
14 ratings
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