Training & Collaboration
There are a number of Biomedical Data Science training opportunities that are available to the Yale community. All Yale credit courses can be found at https://courses.yale.edu/
Download Our Targeted Courses Spreadsheet
We have assembled a useful Spreadsheet of Yale Non-Credit Course, Yale Credited Courses, and Non-Yale Courses.
Introduction to Medical Software
For more information email Xenios Papademetris (firstname.lastname@example.org)
Yale Undergraduate Class “Medical Software Design” (BENG 406b). (Spring Semesters)
The goal of this class is to teach students how to go about designing (and implementing) software for medical purposes. This is a two-track class. In the lectures, we cover material from medical device regulation, risk management, software design, implementation, testing and issues related to the use of machine learning techniques in medical software. In addition, the students are divided into groups that work on a project that is jointly supervised by the instructors and a clinical mentor. As part of this, the students have to understand what the clinical needs are and use this understanding to create formal system and software design documents. They also implement a prototype solution to illustrate the concepts. This allows us to put some of the ideas they learn about in the lectures into practice.
Coursera Online Class: Introduction to Medical Software
This derives from BENG 406b and covers the non-project component of the class. The class is free to take through Coursera. There is a small fee ($50) for those who would like an official certificate. The class consists of 13+ hours of videos – see https://www.medsoftbook.com/the-coursera-class for more information.
Textbook -- Introduction to Medical Software: Foundations for Digital Health, Devices and Diagnostics.
This is a recently published textbook that was written for the needs of BENG 406b and was published in the summer of 2022 by Cambridge University Press. For more information see https://www.medsoftbook.com/the-book
Digital Health: Building Mobile and Wearable Applications for Participatory Health.
Editors: Shabbir Syed-Abdul, Xinxin Zhu, Luis Fernandez-Luque. 2020. Elsevier. eBook ISBN: 9780128200780. Paperback ISBN: 9780128200773.
Personal Health Informatics: Patient Participation in Precision Health
Editors: Pei-Yun Sabrina Hsueh, Thomas Wetter, Xinxin Zhu, Springer; 1st ed. 2022 edition Paperback ISBN 978-3-031-07698-5, Hard Cover ISBN 978-3-031-07695-4, eBook ISBN 978-3-031-07696-1
A list of recommendations on the best places to learn R:
- The “swirl” package. You run it from RStudio directly like you would any other package with install.packages(“swirl”). The benefit here is you get tutorials on R directly from within RStudio. This is how I learned R and I found it to be incredibly helpful. Here is the package link: https://swirlstats.com/students.html
- Coursera course is good as well: https://www.coursera.org/learn/r-programming
- This article also has some good links to useful R-related books and websites (including ggplot2): https://towardsdatascience.com/the-ultimate-r-guide-for-data-science-7d4b6112822a
- All of Hadley Wickham's books:
- ggplot2: Elegant Graphics for Data Analysis - https://ggplot2-book.org/
- R for data science - https://r4ds.had.co.nz (related workshop from rstudio::conf(2019): https://github.com/AmeliaMN/data-science-in-tidyverse
- Advanced R - https://adv-r.hadley.nz (solutions: https://github.com/Tazinho/Advanced-R-Solutions
- And also rstudio::conf materials on github: https://github.com/rstudio/rstudio-conf
- RStudio Education https://education.rstudio.com/learn/
- Software Carpentry
- Tidy Tuesday from R4DS
- Jeff Leek's courses:
- R for Data Science