11 courses with the subject DATA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Data scientists answer questions with scientific and social relevance using statistical theory and computation. We will discuss elementary topics in statistics and learn how to write code (in Python) to visualize data and perform simulations. We will use these tools to answer questions about real data sets. We will also explore ethical issues faced by data scientists today.
This introductory course explores the foundations, implementation, and practical use of artificial intelligence (AI) in data science. Students will examine key historical developments and competing perspectives in the evolution of AI, demystify some of the terms currently dominating public discourse, and gain hands-on experience implementing a simple AI system. We will also use AI tools to handle a variety of data science tasks, exploring different use cases, datasets, and real-world scenarios. Throughout, we will consider both the opportunities and limitations of AI. The course includes a gentle introduction to the Python programming language. No prior knowledge is required.
Artificially intelligent technologies are prominent features of modern life -- as are ethical concerns about their programming and use. In this class we will use the tools of philosophy to explore and critically evaluate ethical issues raised by current and future AI technologies. Topics may include issues of privacy and transparency in online data collection, concerns about social justice in the use of algorithms in areas like hiring and criminal justice, and the goals of developing general versus special purpose AI. We will also look at ethics for AI: the nature of AI "minds," the possibility of creating more ethical AI systems, and when and if AIs themselves might deserve moral rights.
This class is an interdisciplinary course that examines the application of computational tools and methodologies to humanities research, with a strong emphasis on practical Python programming. It covers key topics such as image processing, data visualization, and statistical analysis applied in various domains, including history, archaeology, and the arts. Students engage with diverse case studies and projects, employing computational and statistical techniques to analyze and interpret complex real-world datasets. The course also critically explores methodological challenges in digital humanities, including issues related to sparse data, noisy contexts, and the inherent limits of interpretation.
Fall and Spring. Credits: 1 - 4 Restrictions: Contact instructor for independent study declaration form and signatures.Instructor permission required.
In this course, we study the most pressing ethical concerns relating to emerging technology and envision novel policy solutions to address them. Existing regulatory and policy instruments are often unable to provide sufficient oversight for emerging technology. Can legal anti-discrimination doctrine address biased algorithmic decision-making systems? How does generative artificial intelligence challenge traditional ways of thinking about intellectual property? Do we have rights over the personal data that private firms collect about us? We examine these gaps in the context of contemporary regulatory proposals on national, multinational, and international scales.
This seminar provides an opportunity for students from all disciplines to do guided research using data science tools in a research project of their choice. Students will develop an understanding of the full pipeline of successful data science research by selecting a topic, identifying relevant datasets, designing research methods, conducting in-depth analyses, deriving meaningful conclusions, and submitting a final report. Opportunities for students to present their work and review journal articles create a scaffolded approach. Past project topics include geology, music, demographics, art, economics, government, religion, transportation, and law.
Fall and Spring. Credits: 1 - 8 Restrictions: Contact instructor for independent study declaration form and signatures.Instructor permission required.
Fall and Spring. Credits: 1 - 8 Restrictions: Contact instructor for independent study declaration form and signatures.Instructor permission required.
Source: Mount Holyoke College's catalog, linked per course · table learning_unit · CourseShelf publish 61
