Making Data Science Work for Clinical Reporting

In this module we will introduce this course. We will provide context on clinical reporting in general, describing how clinical trials work at a high level, as well as providing resources to learn more.

In this module we explore how data scientists are able to share their work confidently with the right people. We will look at important concepts related to data and results sharing, quality assurance and data access restrictions.

In this module we explore how to make the most out of data science by developing the best mindset.

In this module we introduce the idea of version control, and git in particular. We show how you can use git effectively to manage your code during clinical reporting, and how it can be used as a tool for collaboration.

In this module we will discuss benefits of InnerSourcing, OpenSourcing and developing our own R packages. We will review some of the core principles and tools of R package development.

In this module we will review the tools and approaches used to understand risk in a codebase used to derive datasets and insights.

In this final module we will briefly review the course, and suggest next steps in your learning journey