Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Manisha Desai, Ph.D.

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.

The course will touch upon aspects of engagement with non-data scientists in a team setting all along the translational research process from study design to data management to data analysis to dissemination of findings. We will address the following questions:

• How should a data scientist be integrated into the team?
• Is there a difference between a data scientist consulting or collaborating on a project?
• When should a data scientist be onboarded to a project, and what happens when the ideal does not occur in practice?
• How should the data scientist engage the collaborator more generally given the different stages at which investigators may be ready to include data science expertise?
• Is data collection, extraction, cleaning and management a topic that should concern a data scientist?
• What do topics like authorship and reasonable timelines have to do with upholding principles of rigor and reproducibility?
• Who is responsible for interpreting empirical findings?

This course touches upon these issues and more in the context of a multidisciplinary team.

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.
• 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 with a guest collaborator.