Ever wanted to use the data you have been collecting for several years now to help better understand your clinical practice? Do you want to learn how best to collet this data, analyse it and see how clinical practice can be improved? Or perhaps you are just interested in what machine learning and AI are. If that is the case, this course is for you!

The Data Analytics in Medicine (DAIM) course will cover the fundamentals of the python programming language that you will need to work with medical data and images. This course will examine the wider implications of these skills on clinical work and considerations that must be made when working with data and code in a clinical setting.

The DAIM course builds on the highly successful Programming in Healthcare course, and is designed to take you to the next level in your digital healthcare journey. Module 3 of the Programming in Healthcare course is a helpful prerequisite to the DAIM course.

The DAIM course is a fantastic opportunity to extend your skills with hands-on workshops and code tasks, with full support from our experienced tutors.

Course Structure

The DAIM course is split into:

Seminars where the core theory for each topic will be covered in easy to digest sessions.

Workshops where you will gain practical experiences of working with the techniques discussed in the seminar, with support from our experienced tutors, working in small groups.

Modules

Module 1 - Introduction Working with the broader concepts required for the course, such as code structure and debugging.

Module 2 Core Python for Image Processing Hands-on experience with core Python modules for image analysis (NumPy, PIL) and performing basic image tasks.

Module 3: Python and DICOM. Working with real-life medical imaging in it’s native format, DICOM.

Module 4 - Machine learning for Medicine Bringing concepts together for the final, most comprehensive unit, where you will build a neural network to analyse chest X-rays using TensorFlow, a machine learning package.