Introduction to Data Science for Healthcare Professionals

Posted 10 months 20 days ago by King's College London

Duration : 4 weeks
Study Method : Online
Subject : Healthcare & Medicine
Overview
Explore the possibilities, concepts, and requirements of working with or alongside big data in the healthcare sector.
Course Description

Discover data analysis concepts and their applications in healthcare with KCL

Data has had transformative effects on healthcare delivery and management, though many professionals in the sector remain unfamiliar with how to gather and interpret data.

On this six-week course from King’s College London, you’ll explore data science concepts, focusing on how data is collected, analysed, and used to inform professional practice in the healthcare sector.

Examine the data science process, including data modelling, analysis, and visualisation

Data science can help answer questions and explore new perspectives.

You’ll be introduced to data science techniques and systems, defining key terms and concepts, before investigating core skills used in data analysis, modelling, and visualisation.

You’ll also gain an overview of the ethical considerations required when handling data in healthcare, and identify some of the risks and challenges of big data analysis.

With this knowledge, you’ll be able to demonstrate how data can be captured, manipulated, and interpreted for meaningful answers in healthcare contexts.

Work effectively with data scientists and data teams

Having examined the terminology and processes used in data science, you’ll identify opportunities for collaboration with data scientists and teams, improving communication and understanding.

You’ll also be able to explain how your own role may interface with data scientists and data teams.

Understand the role of a data management system in storing and structuring data

You’ll learn how data management systems can organise large amounts of data in an easy to access repository and how this can help optimise data use across departments.

By the end of this course, you’ll have gained an overview of data science processes in healthcare contexts, kickstarting your upskilling in a valuable and necessary area.

This course is designed for healthcare professionals with no experience in data science but whose job roles require them to work alongside or lead teams dealing with data science.

It’s suitable for professionals working in both clinical and non-clinical roles including scientists, management, clinicians, nurses, and pharmacists who have access to and manage data projects or large amounts of data.

Requirements

This course is designed for healthcare professionals with no experience in data science but whose job roles require them to work alongside or lead teams dealing with data science.

It’s suitable for professionals working in both clinical and non-clinical roles including scientists, management, clinicians, nurses, and pharmacists who have access to and manage data projects or large amounts of data.

Career Path
  • Explore the common data science techniques and systems and to be able to give clear definitions of them
  • Apply your understanding of data science terms when communicating with data scientists or teams working with data to collaborate effectively
  • Identify where your role may interface within a data science team
  • Develop and effectively word the research question they are trying to answer with their data
  • Identify various data management systems that they may use to store and structure their data
  • Identify the various data analysis techniques you could apply to your data to best answer your question
  • Explore the process of data cleaning required prior to analysing data
  • Describe any further training opportunities that would support you in developing your data science knowledge to apply to health care
  • Understand the importance of data visualization and be able to recognise its value in being able to share data between the analyst, the data expert and patients and public.
  • Critique the risks of using AI and Big Data and the perception this has from patients and public.
  • Identify best practice when using AI and Big Data to minimize risks including understanding the current legislation in place.
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