Introduces the breadth of data science through a project lifecycle perspective. Covers early-stage data-life cycle activities in depth for the development and dissemination of data sets. Provides technical experience with data harvesting, acquisition, pre-processing, and curation. Concludes with an open-ended term project where students explore data availability, scale, variability, and reliability.

This course provides an introduction to the ways that computing systems support social interaction and productive collaboration. Students will learn concepts from social science theory and research and use these concepts to analyze systems and imagine novel system designs that meet the needs of groups and organizations. Students will spend time examining, using, and participating in social and collaborative computing environments such as collaboration tools, crowdwork platforms, social media, and various online communities.

Surveys theory and research literature on socio-technical issues and concepts in computer-supported cooperative work and social computing. Covers topics such as group work in collocated, distributed, and domain-specific contexts; design, implementation and evaluation of collaborative software; social media and online communities; computer-supported collaborative learning and community-learning technologies; and future directions of collaborative and social computing.

Although user experience design has always involved collecting data about users’ needs and preferences, new forms and quantities of user data have created a need for new data analysis skills and professional ethics training among designers. This class introduces students to A/B testing and statistical methods to prepare them to design and run large scale user experiments that can inform design decisions. Students practice using tools and methods as well as composing experiment reports and design recommendations.