• King's College London logo

Introduction to Data Science for Healthcare Professionals

Explore the possibilities, concepts, and requirements of working with or alongside big data in the healthcare sector.

380 enrolled on this course

An animated picture of a group of mixed health care professionals

Introduction to Data Science for Healthcare Professionals

380 enrolled on this course

  • 4 weeks

  • 2 hours per week

  • Digital certificate when eligible

  • Introductory level

Find out more about how to join this course

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.


  • Week 1

    Defining Data Science

    • Welcome to Data Science in Healthcare

      This activity will set the scene for what you are going to learn over the next four weeks. We will introduce the course, who developed and help you learn why data science will be important in your healthcare role.

    • What is Data Science

      We will define data science terms, techniques and systems. This will help you start to recognise the language used in data science teams.

    • The data science team

      There are various roles within the world of data science in healthcare. Let's explore some of them here.

    • Developing questions to ask the data

      How do you develop and effectively word the questions you are asking and trying to answer with the data?

    • Wrap up and summary of week one

      Lets recap what we have learnt in week one and see what we cover in week 2

  • Week 2

    What we can do with all the data available

    • Where this data can be used in healthcare.

      This a summary of the weeks learning and lets listen to some examples of where access to all this data might be helpful in healthcare.

    • Data Basics

      Get to know your data and how to store and structure your data.

    • Data Management

      Effective data management is key to the data analysis pipeline. It is also a task which one is trusted with patients and the public data so being able to use it in best way is important.

    • Summary of the week

      Lets recap the learning objectives of the week.

  • Week 3

    Sharing the results of our data

    • Introduction to the week

      Lets go over what we learn this week, as we start to look data analysis and visualization

    • Introduction to Data Analysis

      Now that you have enough data, which is stored in the best location and is in a format that is easy to use you can start to analyse it.

    • Data Analysis Tools and Skills

      Lets look at some of the tools used in data analysis and where to find more training

    • Data Visualisation

      As we have touched being able to share and communicate your results is a key step in the data analysis pipeline. Lets look into this in more detail.

    • Wrap up and summary of week three

      Lets go over what we have learned this week and see what is to come in week four.

  • Week 4

    Applying data science in healthcare

    • Overview of week four

      Lets see what we will be covering in this final week of the course.

    • Data protection - GDPR

      Health and clinical data is sensitive data so understanding the legislation the governs its use is important.

    • Ethics of Data Science and AI

      Here we will cover some of the risks that come with working with Big data and highlight some ethical concerns of Artificial Intelligence. This will give us a chance to explore the ways these challenges can be mitigated.

    • Case studies

      Lets look at three case studies from various aspects of healthcare to see where the use of data science has made a real impact to care, service provision and research.

    • Wrap up and connect

      Lets review the last week and the whole course.

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • 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.

Who is the course for?

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.

Who will you learn with?

Who developed the course?

King's College London

King’s College London, established in 1829 and a founding college of the University of London, is one of the world’s leading research and teaching universities, based in the very heart of London.

Ways to learn

Subscribe & save

Buy this course

Limited access

Choose the best way to learn for you!

$349.99 for one year

Automatically renews

$134/one-off payment


Develop skills to further your careerFulfill your current learning needSample the course materials
Access to this courseticktick

Access expires 19 Jul 2024

Access to 1,000+ coursestickcrosscross
Learn at your own paceticktickcross
Discuss your learning in commentstickticktick
Certificate when you're eligibleDigital onlyPrinted and digitalcross
Continue & Upgrade

Cancel for free anytime

Ways to learn

Choose the best way to learn for you!

Buy this course

$134/one-off payment

Fulfill your current learning need

  • Access to this course
  • Learn at your own pace
  • Discuss your learning in comments
  • Printed and digital certificate when you’re eligible

Subscribe & save

$349.99 for one year

Automatically renews

Develop skills to further your career

  • Access to this course
  • Access to 1,000+ courses
  • Learn at your own pace
  • Discuss your learning in comments
  • Digital certificate when you're eligible

Cancel for free anytime

Limited access


Sample the course materials

  • Access expires 19 Jul 2024

Find out more about certificates, Unlimited or buying a course (Upgrades)

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps to help you keep track of your learning
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

Want to know more about learning on FutureLearn? Using FutureLearn

Do you know someone who'd love this course? Tell them about it...