This course is aimed to demonstate how principles and methods from data science can be applied in clinical reporting.
Making Data Science Work for Clinical Reporting
Making Data Science Work for Clinical Reporting
Instructors: Dinakar Kulkarni
2,014 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
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Assessments
1 quiz, 6 assignments
Taught in English
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There are 7 modules in this course
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. We will then focus on motivating the course, describing the benefits of applying data science in the context of clinical reporting
What's included
4 videos1 reading1 assignment
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.
What's included
23 videos2 readings1 assignment
In this module we explore how to make the most out of data science by developing the best mindset.
What's included
9 videos1 reading1 quiz1 assignment1 discussion prompt
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. We also look at making an R project in particular reproducible
What's included
18 videos1 reading1 assignment
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. Finally, we will learn how to set up a CI/CD workflow for R package development.
What's included
16 videos3 readings1 assignment1 ungraded lab
In this module we will review the tools and approaches used to understand risk in a codebase used to derive datasets and insights. By the completion of this module you will get some hands on experience applying these principles against a specific open source library.
What's included
5 videos1 assignment1 peer review
In this final module we will briefly review the course, and suggest next steps in your learning journey
What's included
1 video
Instructors
Offered by
Recommended if you're interested in Data Analysis
University of Colorado Boulder
Johns Hopkins University
Wesleyan University
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Frequently asked questions
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