This course introduces students to the core concepts and practices of data science, with a focus on making sense of real-world, messy data. Students will learn how to clean, organize, and analyze data to uncover patterns, trends, and insights. They’ll explore both predictive models, which help anticipate future outcomes, and unsupervised techniques, which reveal natural groupings or structures within the data. Designed to be hands-on and interdisciplinary, the course blends statistics, computing, and critical thinking. It emphasizes practical skill-building through the use of real datasets and widely used tools. In keeping with its social impact focus, the course integrates case studies aligned with the United Nations Sustainable Development Goals (SDGs), covering areas such as public health, education, climate, water security, and institutional governance. Through these cases, students will reflect on how data science can inform policy, improve social programs, and contribute to positive change. By the end of the course, students will be able to work confidently with data, apply analytical techniques, and communicate findings that matter—whether to support decision-making, advance research, or address societal challenges.
