Data Science (HBI111)

Module Objective

Modern biomedical data science addresses large and complex data sets covering many different areas of biological and clinical data. This data, if analysed appropriately, is a valuable resource for improving patient diagnoses and treatment, and reviewing policy and strategy and current practice. It can also be used to support service improvements and innovation. However, the effective use of this data requires significant skills in the science of the handling and analysis of complex data.

By the end of this module the Clinical Scientist in HSST will be able to analyse, synthesise and apply their knowledge and understanding and ability to work with large, complex data sets, including use of appropriate statistical and mathematical tools for extracting and validating meaning from the data. They will also be able to incorporate unstructured data into a structured, manageable framework. The Clinical Scientist in HSST will also be expected to consistently demonstrate the attitudes and behaviours necessary for the role of a CCS.

By the end of this module the Clinical Scientist in HSST will be able to analyse, synthesise, evaluate and critically apply their expert knowledge to the following:

  • Structured data.
  • Unstructured data.
  • Use of text analysis methods.
  • Integration of multiple data sources.
  • Quality control of data.
  • Data and metadata.
  • Analysis and mining of data – predictive modelling, machine learning.
  • Data models.
  • Data types.
  • Data visualisation – techniques, software and presentation style.
  • Integrated governance and decision-making process in the health context.
  • Safeguarding person data, confidentiality, privacy.
  • Risk of non-identifiable information being cross-referenced with other databases to make it possible to identify individuals.
  • Assessment of risk.
  • Error analysis and error reduction, e.g. signal processing.

By the end of this module the Clinical Scientist in HSST will have a critical understanding of current evidence and its application to the performance and mastery of a range of technical skills and will be able to:

  • Take unstructured data and create structured frameworks with quality assessment and annotation.
  • Use statistical measures to describe and explore a data set.
  • Use data resource linkage methods.
  • Visualise and communicate data in a broadly interpretable form that others without advanced statistical skills can appreciate.
  • Identify, apply and integrate appropriate data resources.

By the end of this module the Clinical Scientist in HSST will be expected to critically reflect and apply in practice a range of clinical and communication skills with respect to data science, for example in using data to support service innovation or using the data to support evidence-based analysis of treatment effectiveness. They will communicate effectively with the public, patients, clinicians, academics and other healthcare professionals, and will be able to:

  • Explain and justify statistical modelling choices.
  • Communicate the results effectively, to both technical and non-technical audiences.
  • Provide and explain a structured data set for clinical application.

In addition Clinical Scientists in HSST will be aware of their own attitudes, values, professional capabilities and ethics, and critically reflect on: (i) their professional practice; and (ii) the challenges of applying research to practice in relation to these areas of practice, identifying opportunities to improve practice building on a critique of available evidence.