Introduces practical skills in data analysis and visualization and enhances AI literacy, tailored for the fields of Classics and ancient studies. Students explore applications such as mapping ancient sites, performing statistical analyses on historical data, and conducting linguistic analyses of classical texts, and becoming proficient conducting data-supported interdisciplinary research.
Additional Requirements for Graduate Students: Graduate students will complete an independent research project on a topic of their own choosing. This project will require (1) extensive research beyond the readings assigned for undergraduates, (2) analysis of trends in contemporary scholarship, and (3) an original argument based on integrating and synthesizing a wide range of primary and secondary material. Graduate students will also prepare an in-class presentation of their research and lead the rest of the class in discussion.
Athena Title
Data Analytics for Classics
Prerequisite
CLAS 1000 or CLAS 1000E or CLAS 1000H or CLAS 1010 or CLAS 1010E or CLAS 1010H or CLAS 1020 or CLAS 1020E or CLAS 1020H or CLAS 3000 or CLAS 3010 or CLAS(ANTH) 3015 or CLAS(ANTH) 3015E or CLAS 3030 or CLAS 3040 or CLAS3050 or permission of department
Grading System
A - F (Traditional)
Student learning Outcomes
By the end of this course, students will be able to compile datasets from historical, archaeological, and literary sources.
By the end of this course, students will be able to generate charts, graphs, and maps that effectively communicate complex data trends in ancient studies.
By the end of this course, students will be able to demonstrate proficiency with Python and Geographical Information Systems (GIS) tools for analyzing and visualizing historical datasets.
By the end of this course, students will be able to formulate data-supported interpretations of ancient texts through keyword, frequency, and concordance analysis.
By the end of this course, students will create a final project in which they apply data analysis and visualization techniques to a specific topic in Classics or ancient studies.
Topical Outline
I. Data Analysis in the Humanities
II. Data Handling and Cleaning for Historical Datasets
III. Data Visualization Techniques
IV. Geographical Information Systems (GIS) in Classics
V. Textual Analysis and Corpus Linguistics
VI. Network Analysis in Ancient Studies
VII. Introduction to Python for Data Analysis in Classics
VIII. Generative AI and AI-Assisted Textual Analysis in Classics
IX. Temporal Analysis and Timelines in Classics
X. Digital Humanities and Interdisciplinary Research
XI. Ethical Considerations in Digital Humanities and AI
XII. Interactive Worldbuilding and Gaming as Public Humanities
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
