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Innovation Scholars Programme: Big Data Skills Training




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Who is Innovation Scholars for?

Funded by UKRI, Kings has launched a new flexible and modular training programme for all health care professionals, researchers and industry partners. This training aims to upskill all in their ability to use and apply big data in their work and research.

Find out more about the mission and aims of the Innovation Scholars programme here: innovationscholars.er.kc.ac.uk/about

Training in 'Big Data'

The training provided through Innovation Scholars is grouped into three pillars:

Most of the modules are aimed at the beginner level, but there are some opportunities to learn more at the intermediate and advanced skill levels. Some modules may require you to bring your own data to use in the training.

Find out more about the Training Pillars and specific courses here: innovationscholars.er.kcl.ac.uk/training

What is the time commitment?

This online training is designed to be flexible and modular, taking into account the already busy schedules of those who work in the NHS, research and industry. Training module are designs so that participants can complete the training in a time frame that suits them.


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For more information contact Emily Robinson, Innovation Scholars Programme Manager.

Pillar 1: Health Data Science

Modules under this pillar will introduce and train you to use a variety of large data analysis techniques, allowing you to process the ever-growing bank of electronic health record data for both research and real world applications.

Using a combination of your own and exemplar data these training modules offer hands on learning with the chance to apply your newly acquired skills. Modules will cover themes including health informatics, statistical programming, data science, modelling, natural language processing and more.

This training offers flexible and modular skill development pathways for researchers and all staff which work in health (clinical or non clinical) at different career stages, skill levels and sectors within health sciences. 

Click here to find out more.


  • Introduction to R

  • Machine Learning

  • Prediction Modelling

  • Introduction to AI/Deep Learning

  • Natural Language Processing

Click here to find out more details about these courses.

Other courses in development include: 

  • Introduction to Python 

  • Causal Modelling 

  • Statistical Modelling 

  • Longitudinal data analyses 

  • Structural Equation Modelling 


Pillar 2: ‘Omics

The vast amounts of data being produced in the life sciences means there are several hundred web-accessible databases for researchers to navigate and understand how to select and extract reliable data from these complex large-scale datasets.

These different ‘omics’ data types can only be analysed with specialized computational tools meaning interdisciplinary projects where various teams need to work together with bioinformaticians to answer health research questions is key. This is even more important when different ‘omics’ data types are integrated together. Working with bioinformaticians, researchers and health professionals need the vocabulary and acquire basic skills to communicate effectively with bioinformaticians and better comprehend the crucial steps of the analysis of complex large-scale data allowing effective collaboration.

In the ‘Omics pillar there is a series of high-quality workshops, that are linked and integrated but can be taken separately and in any order at two different competency levels, beginner and applied. Most workshops are hands-on and have a ‘bring your own data’ to analyse component.

Click here to find out more.


  • Online resources to access published datasets and basic biological data  

  • Using spreadsheets for recording data and metadata

  • Using R for data manipulation – data integration – data visualization

  • Unix and IT infrastructure

  • Statistics with R 

  • How to run NGS pipelines 

Click here to find out more details about these courses.


Future anticipated courses: 

  • R course (advanced level) 

  • Unix course (advanced level - including “high-performance computing” supercomputers) 

  • How to share your code on GitHub 

  • How to make your code reproducible and informative using Rmarkdown 

  • Data integration 

  • Designing Big Data Experiments for successful bioinformatic analysis 

  • Image analysis, phenotyping and integration with genomics and transcriptomics data 


Pillar 3: Artificial Intelligence

Artificial intelligence has already transformed many industries and is about to do the same to healthcare. However, there is a gap between real world clinical care and AI which this training aims to bridge. These three modules will appeal to those interested in evidence-based medicine, extracting knowledge from large-scale data and applying AI to improve patient care.

This training is led by the AI Centre for Value Based Health care (AI4VBH) which is a cross-institutional initiative pooling expertise from across the healthcare ecosystem, including technology companies to deliver AI, data science and advanced research into clinical practice. The AI pillar is partnered with NVIDIA on Cambridge-1, the UK’s most powerful supercomputer dedicated to healthcare and life sciences research, which will focus on solving large-scale healthcare and data-science problems.

Click here to find out more.


  • Demystifying AI

  • Software Carpentry with Python

  • Applied Artificial Intelligence

Click here to find out more details about these courses.


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