- Foundations in Mathematics and Computing
- Computational and Inferential Depth
- Modeling, Learning and Decision Making
- Domain Emphasis
- Human Contexts and Ethics
The Data Science Major and upcoming Minor programs come in response to intensifying student, faculty, business, and societal demand amid the exponential growth of data in virtually all aspects of life. This transformation is generating a substantial unmet need for graduates who are not only technically proficient in analyzing data but who also know how to responsibly collect and manage data, and use data to make decisions and discoveries, think critically, and communicate effectively.
The Data Science B.A., designed in collaboration with faculty from across the University, invests students with deep technical knowledge, expertise in how to apply that knowledge in a field of their choosing, and an understanding of the social and human contexts and ethical implications of how data are collected, analyzed and used. This combination positions graduates to help inform and develop solutions to a range of pressing challenges, from adapting industry to a new world of data, to amplifying learning in education, to helping communities recover from disaster.
Data science combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.
The Data Science degree programs equip students to draw sound conclusions from data in context, using knowledge of statistical inference, computational processes, data management strategies, domain knowledge, and theory. Students learn to carry out analyses of data through the full cycle of the investigative process in scientific and practical contexts. They also gain a deep appreciation of the human, social, and institutional structures and practices that shape technical work around computing and data, as well as an understanding of how data, data analytics, machine learning, artificial intelligence, and computing permeate and shape our individual and social lives.