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This independent study offers students the opportunity to participate in the WiDS Datathon for 1-unit of credit. The WiDS Datathon is an annual and global event that encourages data scientists of all levels to discover and hone their data science skills while solving an interesting and critical social impact challenge. The 2023 Challenge, "Data Science for Subseasonal Forecast", centers on climate change and is in partnership with Climate Change AI (CCAI). Accurate long-term forecasts of temperature and precipitation is crucial for mitigating the effects of climate change (i.e. preparing for droughts and other wet weather extremes). Such forecasts can potentially impact many industries (e.g. agriculture, energy, disaster planning) in countries across the globe. Currently, purely physics-based models dominate short-term weather forecasting. But these models have a limited forecast horizon. The availability of meteorological data offers an opportunity for data scientists to improve subseasonal forecasts by blending physics-based forecasts with machine learning. To learn more, visit: https://www.widsconference.org/datathon.htmlStudents may participate in this independent study in teams of 1-4. To qualify for official participation in the datathon, at least half of each team must identify as women. To receive credit, the team will participate in the Datathon and write a report detailing their submission and reflecting on their experience. Interested students should register for the course, and sign up as a team using this form: https://forms.gle/LyX3yNU7dLnTCux1A. To find other students interested in forming a team, go here: https://docs.google.com/presentation/d/1UvutEFtYFeCkLkwnpU01R5V5WmJeMi4kVkaZYHxSiAY/edit?usp=sharing

( COMM 177B is offered for 5 units, COMM 277B is offered for 4 units.) This class will tackle data-driven journalism, in collaboration with other academic and journalistic partners. The class is centered around one or more projects rooted in local data-driven journalism but with potential for regional or national journalistic stories and impact. Students work in interdisciplinary teams to negotiate for public records and data, analyze data and report out stories. Some of the work may be published by news organizations or may be used to advance data journalism projects focused on public accountability. Students will gain valuable knowledge and skills in how to negotiate for public records, how to critically analyze data for journalistic purpose and build out reporting and writing skills. Students with a background in journalism (especially data journalism), statistics, computer science, law, or public policy are encouraged to participate. Enrollment is limited. May be repeated for credit. (Cardinal Course certified by the Haas Center)

Graduate students register for COMM 277I. COMM 177I is offered for 5 units, COMM 277I is offered for 4 units.) Learn how to apply an investigative and data mindset to journalism, from understanding how to background an individual or entity using online databases to compiling or combining disparate sets of information in ways that unveil wrongdoing or mismanagement. Focuses on mining texts, tracking associations, and using visualizations. Stories produced apply investigative techniques to beat reporting, breaking news, and long form journalism. Instructor permission required for freshmen and sophomores. (Cardinal Course certified by the Haas Center)

( COMM 177B is offered for 5 units, COMM 277B is offered for 4 units.) This class will tackle data-driven journalism, in collaboration with other academic and journalistic partners. The class is centered around one or more projects rooted in local data-driven journalism but with potential for regional or national journalistic stories and impact. Students work in interdisciplinary teams to negotiate for public records and data, analyze data and report out stories. Some of the work may be published by news organizations or may be used to advance data journalism projects focused on public accountability. Students will gain valuable knowledge and skills in how to negotiate for public records, how to critically analyze data for journalistic purpose and build out reporting and writing skills. Students with a background in journalism (especially data journalism), statistics, computer science, law, or public policy are encouraged to participate. Enrollment is limited. May be repeated for credit. (Cardinal Course certified by the Haas Center)

Graduate students register for COMM 277I. COMM 177I is offered for 5 units, COMM 277I is offered for 4 units.) Learn how to apply an investigative and data mindset to journalism, from understanding how to background an individual or entity using online databases to compiling or combining disparate sets of information in ways that unveil wrongdoing or mismanagement. Focuses on mining texts, tracking associations, and using visualizations. Stories produced apply investigative techniques to beat reporting, breaking news, and long form journalism. Instructor permission required for freshmen and sophomores. (Cardinal Course certified by the Haas Center)

Get real-world experience researching and developing your own social impact project! Students work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, community discussions, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Application required; please see cs51.stanford.edu for more information.

Continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public. Prerequisite: CS 51, or consent of instructor.

Survey course on applications of fundamental computer science concepts from CS 106B to problems in the social good space (such as health, trust & safety, government, security, education, and environment). Each week consists of in-class activities designed and delivered by student instructors. Introduces students to JavaScript and the basics of web development. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, the basics of open source software, and principles of cybersecurity. For more information, visit cs106s.stanford.edu. Pre/Corequisite: CS106B. Cardinal Course certified by the Haas Center for Public Service

This project-based course aims to bring together students from computer science and the social sciences to work with external partner organizations at the nexus of digital technology and public policy. Students will collaborate in interdisciplinary teams on a problem with a partner organization. Along with the guidance of faculty mentors and the teaching staff, students will engage in a project with outcomes ranging from policy memos and white papers to data visualizations and software. Possible projects suggested by partner organizations will be presented at an information session in early March. Following the infosession, a course application will open for teams to be selected before the start of Spring Quarter. Students may apply to a project with a partner organization or with a preformed team and their own idea to be reviewed for approval by the course staff. There will be one meeting per week for the full class and at least one weekly meeting with the project-based team mentors. Prerequisites: Appropriate preparation depends on the nature of the project proposed, and will be verified by the teaching staff based on your application.