Computational Social Science Training Program

项目摘要/摘要 加州大学伯克利分校的计算社会科学培训计划(CSSTP)提供高级 面向学习健康主题的社会和行为科学博士后 尤尼斯·肯尼迪·施莱弗国家儿童与人类发展研究所。CSSTP是一个新的计划， 将伯克利在定量社会和行为科学方面的长期实力与其在全国范围内的- 在数据科学教育、实践和研究方面获得认可的校园计划。它将可供五人入场 五年内每年培训一名学员。培训人员包括22名具有模范记录的社会科学家 开发和应用新的统计方法来解决与健康有关的社会/行为科学问题，如 以及13位数据科学家，他们是数学、统计学/生物统计学基础方面的领军人物，以及 计算机科学。受训者将从六名社会科学博士的不同学生中挑选 计划，提供了严格和量身定做的计划，旨在教授团队以科学为基础的方法 为了解决问题，强调对密集或大量的纵向数据和高密度的分析， 大型样本或总体水平的机构数据库。每个实习生都有一个双教官模型支持 S/他被提供一名社会科学教师导师和一名数据科学导师，帮助促进 实习生在整个项目中的进步。CSSTP学员在伯克利分校获得社区空间 数据科学研究所(BIDS)，一个动态的多学科数据科学研究中心，学员 与其他常驻数据科学研究员一起工作。在完成第一年的课程要求后 他们的国内部门，实习生在他们研究生院的第二年正式进入这个项目，设计一个 个人发展计划，并参加计算社会科学的两学期核心课程，团队授课 通过培训教师。本课程向学生介绍基本的数据科学方法和工具，包括 Python编程、数据管理、自然语言处理、机器学习、因果推理和 负责任的行为和研究的再现性，通过讲座，深入讨论社会科学 应用程序和小组学习练习。在接下来的一年里，学生们通过 在校园和/或与外部行业合作的健康相关研究团队或实验室中的安置 合作伙伴，从而通过涉及开发和开发的研究实践发展高级分析技能 实施新办法。根据学生的需要和兴趣提供的额外培训通过 选修课、每周一次的计算社会科学研讨会系列，以及 伯克利数据科学研究所和社会科学D-Lab，数据科学培训和 面向社会科学家的研究。CSSTP的好处将通过以下方式波及更大的校园和更远的地方 新的教员合作，并通过创建大量经过严格培训的计算社会科学 在快速变化和发展的数据密集型领域具有竞争力和胜任工作的学生。

Project Summary/Abstract The Computational Social Science Training Program (CSSTP) at UC Berkeley provides training in advanced analytics to predoctoral students in the social and behavioral sciences studying health topics covered by the Eunice Kennedy Shriver National Institute for Child and Human Development. CSSTP is a new program that combines Berkeley's long-standing strength in quantitative social and behavioral science with its nationally- recognized campus programs in data science education, practice, and research. It will serve five entering trainees per year over five years. The training faculty includes 22 social scientists who have exemplary records of developing and applying novel statistical methods to health-related social/behavioral science problems, as well as 13 data scientists who are leading figures in the foundations of mathematics, statistics/biostatistics, and computer science. Trainees, who will be drawn from a diverse pool of students in six social science doctoral programs, are provided with a rigorous and tailored program designed to teach a team science-based approach to problem solving and to emphasize the analysis of intensive or voluminous longitudinal data and high-density, large sample or population level agency databases. Each trainee is supported by a dual-preceptor model in which s/he is provided with a social sciences faculty mentor and a data science mentor who help to facilitate the trainee's progress through the program. CSSTP trainees are provided with community space at the Berkeley Institute for Data Science (BIDS), a dynamic multi-disciplinary data science research center, where trainees work alongside other data science fellows in residence. After completing their first-year course requirements in their home departments, trainees formally enter the program in their second year of graduate school, devise an individual development plan, and take a core two-semester course in computational social science, team-taught by training faculty. This course introduces students to essential data science methods and tools, including Python programming, data management, natural language processing, machine learning, causal inference, and responsible conduct and reproducibility of research, through lectures, in-depth discussion of social science applications, and small group learning exercises. In the following year, students apply these skills through placements on collaborative health-related research teams or labs on campus and/or with external industry partners, thus developing skills in advanced analytics through research practice involving the development and implementation of new methods. Additional training tailored to student needs and interests is provided through elective courses, a weekly computational social science workshop series, and ongoing working groups at the Berkeley Institute for Data Science and the Social Science D-Lab, a campus hub for data science training and research for social scientists. CSSTPs benefits will ripple out to the greater campus and beyond by stimulating new faculty collaborations and by creating a critical mass of rigorously trained computational social science students who will be competitive and qualified for jobs in rapidly changing and evolving data intensive fields.