Curriculum for Digital Humanities Degree

Champlain College's Digital Humanities curriculum blends technology and the humanities, offering hands-on learning and adaptability for a dynamic future.

Our Upside-Down curriculum in Digital Humanities is designed to empower you to explore the exciting intersection of technology and the humanities. You’ll engage in a dynamic and hands-on learning experience that combines critical thinking, creativity, and technical skills such as programming and digital mapping. With a customizable program, you’ll have the freedom to choose from a wide range of courses, develop your own digital toolkit, and engage in project-based learning with faculty guidance. You’ll gain hands-on experience and build a strong foundation in areas such as data analysis, multimedia storytelling, and cultural preservation.

Learning Outcomes for Digital Humanities

This curriculum is an example of possible courses in this major. Always check the college catalog for the exact requirements.

Digital Humanities Core
- DHS 2XX Digital Humanities
- DHS 3XX Applied AI Ethics
- DHS 4XX Designing Digital Futures
Analyzing Data
- CSI 160 Python Programming
- DAT 210 Data Analytics
- DAT 230 Data Visualization —OR— DAT 330 Data Mining
Skills Development

Students choose a skill pack with a humanities, technology, or digital media focus to develop specific skills and methods in their area of interest.

Students will learn the history and basics of computing as well as the fundamentals of Python programming. General topics include: the history of computing, number systems, Boolean logic, algorithm design and implementation, and modern computer organization. Programming topics include: memory and variables, data types, mathematical operations, basic file I/O, decision-making, repetitions, functions, and list basics.

Prerequisites
Credits	3

Students will analyze data using Python programming language to obtain insights from real-world data sets. Students will access data files containing both numerical and categorical data, which they will prepare for analysis by data preprocessing, data wrangling, and data aggregation. Students will conduct statistical analysis, text analysis, and date/time series analysis, summarize their findings in a meaningful manner, and use visualization to illustrate patterns and trends in the data.

Prerequisites	Complete MTH-180 and any CSI introductory programming course
Credits	3

Data visualization-representing complex data in a visual format-is a way to communicate and explore patterns, trends, and relationships in a data set. In this course, students will examine a variety of data sets to create data visualizations and use them to explore, interpret, and present information. After transforming data sets into visual graphics, students will then create business reports and presentations designed to drive effective decision-making.

Prerequisites	Complete 15 credits
Credits	3

This course introduces students to data mining and its various applications. Students will gain proficiency in supervised data mining techniques for building prediction models such as decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross-validation and using entropy measures for weighting features. Students will also use unsupervised data mining techniques such as clustering and association analysis. Both methods are used for discovering patterns and associations in data.

Prerequisites	Complete DAT-210
Credits	3

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