The practice of data science is inherently a collaborative endeavor, and this course aims to equip data scientists and aspiring data scientists with the necessary skills for effective teamwork in clinical and translational research. The principles discussed in the course apply not only to the medical field but also to the behavioral and social sciences. Throughout the course, participants will explore various aspects of team engagement that are critical for the entire translational research process from study design to data management to data analysis to dissemination of findings. Key questions addressed include how to identify the required expertise for a team, how to integrate and onboard data scientists into teams, how to engage collaborators effectively by successfully leveraging multiple disciplines to jointly solve problems, how to educate the team on the role of the data scientist, as well as the data scientist's role in data collection, cleaning, and management. The course also delves into issues that impact rigor and reproducibility such as authorship, reasonable timelines, interpreting empirical findings, and the importance of statistical analysis plans and study registration. Material is taught through lectures, simulated role-playing exercises, and real-time demonstrations to enhance learning and practical application. Data scientists are working more and more as part of scientific teams. In this course, participants who are (or who are training to be) data scientists will learn optimal team science tools for engaging clinical and translational investigators in the collaborative research process. These principles apply across the medical, behavioral, and social sciences.Topic areas include: optimal team make up from a data science perspective; how to engage collaborators on study design; how to educate collaborators on engaging data scientists; how to educate collaborators on rigor and reproducibility principles such as creating a statistical analysis plan, pre-registering studies, and deciding on authorship; elements that comprise the ideal statistical analysis plan; how to play an integral role during data collection and data extraction phases of the study; and optimal approaches for dissemination of findings to the team and to the research community that adhere to rigor and reproducibility principles and that ensure integration of the data scientist?s voice. In addition to lectures, materials will be taught using simulated role playing and real-time demonstrations of collaborations.
2 units · Medical Satisfactory/No Credit
The practice of data science is inherently a collaborative endeavor, and this course aims to equip data scientists and aspiring data scientists with the necessary skills for effective teamwork in clinical and translational research. The principles discussed in the course apply not only to the medical field but also to the behavioral and social sciences. Throughout the course, participants will explore various aspects of team engagement that are critical for the entire translational research process from study design to data management to data analysis to dissemination of findings. Key questions addressed include how to identify the required expertise for a team, how to integrate and onboard data scientists into teams, how to engage collaborators effectively by successfully leveraging multiple disciplines to jointly solve problems, how to educate the team on the role of the data scientist, as well as the data scientist's role in data collection, cleaning, and management. The course also delves into issues that impact rigor and reproducibility such as authorship, reasonable timelines, interpreting empirical findings, and the importance of statistical analysis plans and study registration. Material is taught through lectures, simulated role-playing exercises, and real-time demonstrations to enhance learning and practical application. Data scientists are working more and more as part of scientific teams. In this course, participants who are (or who are training to be) data scientists will learn optimal team science tools for engaging clinical and translational investigators in the collaborative research process. These principles apply across the medical, behavioral, and social sciences.Topic areas include: optimal team make up from a data science perspective; how to engage collaborators on study design; how to educate collaborators on engaging data scientists; how to educate collaborators on rigor and reproducibility principles such as creating a statistical analysis plan, pre-registering studies, and deciding on authorship; elements that comprise the ideal statistical analysis plan; how to play an integral role during data collection and data extraction phases of the study; and optimal approaches for dissemination of findings to the team and to the research community that adhere to rigor and reproducibility principles and that ensure integration of the data scientist?s voice. In addition to lectures, materials will be taught using simulated role playing and real-time demonstrations of collaborations.
Offered in Autumn 2025, Winter 2026, Spring 2026 at Stanford University.