Data Science in Stratified Healthcare and Precision Medicine

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Data Science in Stratified Healthcare and Precision Medicine

Instructors: Dr Areti Manataki

31,533 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

341 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

341 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments¹

AI Graded see disclaimer

Taught in English

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There are 5 modules in this course

An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data, to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare.

In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine. You will have a hands-on experience of working with such data. And you will learn from leaders in the field about successful case studies. Topics include: (i) Sequence Processing, (ii) Image Analysis, (iii) Network Modelling, (iv) Probabilistic Modelling, (v) Machine Learning, (vi) Natural Language Processing, (vii) Process Modelling and (viii) Graph Data. Watch the course promo video here: http://edin.ac/2pn350P Learners can apply for Financial Aid directly with Coursera to assist with the cost of accessing the full course and gaining a certificate for successfully completing the course.

Join us this week to find out how the course works and to try your hand at programming in Python!

What's included

7 videos8 readings1 assignment2 ungraded labs

7 videos Total 52 minutes

Welcome to the Course 3 minutes
About the Course 3 minutes
Demystifying Data Science 4 minutes
Python Basics Part 1 9 minutes
Python Basics Part 2 5 minutes
Professor Andrew Morris 12 minutes
Professor Aileen Keel 15 minutes

8 readings Total 80 minutes

Syllabus 10 minutes
Course Logistics 10 minutes
How to use the Discussion Forums 10 minutes
Course Team 10 minutes
Acknowledgements 10 minutes
Copyright 10 minutes
How to use Jupyter Notebooks 10 minutes
Programming tips & tricks 10 minutes

1 assignment Total 30 minutes

Quiz 1 30 minutes

2 ungraded labs Total 120 minutes

Python Basics Part 1 60 minutes
Python Basics Part 2 60 minutes

This week you will be introduced to Sequence Processing and Medical Image Analysis. Explore the course materials to find out about recent advances in these areas and how they contribute to Precision Medicine!

What's included

9 videos1 assignment1 ungraded lab

9 videos Total 53 minutes

Introduction 2 minutes
DNA and Sequencing 4 minutes
Modelling the Data 3 minutes
Conclusion 4 minutes
Professor Tim Aitman 14 minutes
Professor David Porteous 11 minutes
Introduction 1 minute
Medical Imaging Data & Modalities 8 minutes
Analysing Medical Images 6 minutes

1 assignment Total 30 minutes

Quiz 2 30 minutes

1 ungraded lab Total 60 minutes

Image Analysis Programming Task 60 minutes

This week you will learn about Probabilistic and Network Modelling, and how they are applied to biomedicine. You will also be introduced to Machine Learning and explore the opportunities it brings to the medical field.

What's included

11 videos1 reading2 assignments2 ungraded labs

11 videos Total 43 minutes

Introduction 2 minutes
Representing Networks 4 minutes
Biological Networks 5 minutes
Conclusion 2 minutes
Introduction 2 minutes
Statistical Methods in Medical Research 4 minutes
Conclusion 4 minutes
Introduction 2 minutes
Supervised Learning 11 minutes
Unsupervised Learning 6 minutes
Conclusion 2 minutes

1 reading Total 10 minutes

How the Programming Assignment works 10 minutes

2 assignments Total 60 minutes

Quiz 3 30 minutes
Programming Assignment Quiz 30 minutes

2 ungraded labs Total 120 minutes

Machine Learning Programming Task 60 minutes
Programming Assignment Notebook 60 minutes

This week you will discover how clinical notes and other free-form text can be analysed with the use of Natural Language Processing techniques. You will also find out how Process Modelling can help us understand, stratify and improve healthcare processes.

What's included

9 videos1 reading1 assignment1 ungraded lab

9 videos Total 57 minutes

Introduction 3 minutes
Tasks 10 minutes
Computational Methods 4 minutes
Angus McCann from IBM 8 minutes
Introduction 2 minutes
Modelling Processes 7 minutes
Analysing Processes 6 minutes
Process Mining 3 minutes
Rodrigo Barnes from Aridhia 14 minutes

1 reading Total 10 minutes

IBM Watson 10 minutes

1 assignment Total 30 minutes

Quiz 4 30 minutes

1 ungraded lab Total 60 minutes

Natural Language Processing Programming Task 60 minutes

In this final week of the course you will learn how the Graph Data model allows for effective linkage of different data in the life sciences. You will also explore societal, legal and ethical implications of precision medicine and stratified healthcare.

What's included

7 videos4 readings1 assignment1 peer review1 ungraded lab

7 videos Total 40 minutes

Introduction 3 minutes
Graph Data & RDF 7 minutes
Ontologies & Graph Data Conclusion 5 minutes
Dr Steve Pavis 8 minutes
Professor Mark Parsons 8 minutes
Society, Law and Ethics 6 minutes
Course Conclusion 3 minutes

4 readings Total 40 minutes

SPARQL Querying 10 minutes
General Data Protection Regulation (GDPR) 10 minutes
Research Ethics 10 minutes
Post-Course Survey 10 minutes

1 assignment Total 30 minutes

Quiz 5 30 minutes

1 peer review Total 120 minutes

Final Course Assessment 120 minutes

1 ungraded lab Total 60 minutes

Graph Data Programming Task 60 minutes

Instructors

Instructor ratings

(88 ratings)

Dr Areti Manataki

The University of Edinburgh

4 Courses 675,209 learners

Dr Frances Wong

The University of Edinburgh

1 Course 31,533 learners

Offered by

The University of Edinburgh

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Learner reviews

5 stars
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3.51%
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1 star
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Reviewed on Jul 5, 2023

Course is very good to understanding and instructors are highly experianced.I gain a lot from this course.

Reviewed on Oct 30, 2019

An excellent introduction to Data Science in healthcare! Overall, this course was very knowledgeable, interactive, challenging and fun!Thank you Coursera!

Reviewed on Mar 14, 2023

Great course to gain an introductory understanding of domain knowledge in Healthcare using Data Science!

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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.