Computational Social Science Training Program

项目总结/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 分析的社会和行为科学研究所涵盖的健康主题的博士前学生 尤尼斯·肯尼迪·施莱佛国家儿童和人类发展研究所。CSSTP是一个新的程序， 结合了伯克利在定量社会和行为科学方面的长期优势， 在数据科学教育，实践和研究方面公认的校园计划。它将服务于五个进入 五年内每年培训一名学员。培训教师包括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，以健康相关的社会/行为科学问题， 以及13位数据科学家，他们是数学，统计学/生物统计学基础的领军人物， 计算机科学受训人员将从六个社会科学博士学位的不同学生中挑选 计划，提供了一个严格的和量身定制的计划，旨在教一个团队的科学为基础的方法 解决问题，并强调密集或大量纵向数据和高密度的分析， 大样本或人口水平机构数据库。每个学员都得到双导师模式的支持， 他/她被提供了一个社会科学教师导师和一个数据科学导师，他们帮助促进 学员在项目中的进步CSSTP学员提供社区空间在伯克利分校 数据科学研究所（BIDS），一个动态的多学科数据科学研究中心， 与其他数据科学研究员一起工作。在完成第一年的课程要求后， 他们的家庭部门，学员正式进入程序在他们的研究生院的第二年，设计一个 个人发展计划，并采取核心的两个学期的课程，在计算社会科学，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， 负责任的行为和研究的可重复性，通过讲座，社会科学的深入讨论 应用程序和小组学习练习。在接下来的一年里，学生们通过以下方式应用这些技能： 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职位 合作伙伴，从而通过涉及开发和 实施新方法。根据学生的需求和兴趣提供额外的培训， 选修课程，每周一次的计算社会科学研讨会系列，以及正在进行的工作组， 伯克利数据科学研究所和社会科学D-Lab，数据科学培训和 社会科学家的研究。CSSTPs的好处将涟漪到更大的校园和超越刺激 新的教师合作，并通过创造一个严格训练的计算社会科学的临界质量 学生谁将在快速变化和不断发展的数据密集型领域的工作竞争力和资格。

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.