Data Science in Stratified Healthcare and Precision Medicine

Data Science in Stratified Healthcare and Precision Medicine

Instructors: Dr Areti Manataki

31,718 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 videosTotal 52 minutes

Welcome to the Course3 minutes
About the Course3 minutes
Demystifying Data Science4 minutes
Python Basics Part 19 minutes
Python Basics Part 25 minutes
Professor Andrew Morris12 minutes
Professor Aileen Keel15 minutes

8 readingsTotal 80 minutes

Syllabus10 minutes
Course Logistics10 minutes
How to use the Discussion Forums10 minutes
Course Team10 minutes
Acknowledgements10 minutes
Copyright10 minutes
How to use Jupyter Notebooks10 minutes
Programming tips & tricks10 minutes

1 assignmentTotal 30 minutes

Quiz 130 minutes

2 ungraded labsTotal 120 minutes

Python Basics Part 160 minutes
Python Basics Part 260 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 videosTotal 53 minutes

Introduction2 minutes
DNA and Sequencing4 minutes
Modelling the Data3 minutes
Conclusion4 minutes
Professor Tim Aitman14 minutes
Professor David Porteous11 minutes
Introduction1 minute
Medical Imaging Data & Modalities8 minutes
Analysing Medical Images6 minutes

1 assignmentTotal 30 minutes

Quiz 230 minutes

1 ungraded labTotal 60 minutes

Image Analysis Programming Task60 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 videosTotal 43 minutes

Introduction2 minutes
Representing Networks4 minutes
Biological Networks5 minutes
Conclusion2 minutes
Introduction2 minutes
Statistical Methods in Medical Research4 minutes
Conclusion4 minutes
Introduction2 minutes
Supervised Learning11 minutes
Unsupervised Learning6 minutes
Conclusion2 minutes

1 readingTotal 10 minutes

How the Programming Assignment works10 minutes

2 assignmentsTotal 60 minutes

Quiz 330 minutes
Programming Assignment Quiz30 minutes

2 ungraded labsTotal 120 minutes

Machine Learning Programming Task60 minutes
Programming Assignment Notebook60 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 videosTotal 57 minutes

Introduction3 minutes
Tasks10 minutes
Computational Methods4 minutes
Angus McCann from IBM8 minutes
Introduction2 minutes
Modelling Processes7 minutes
Analysing Processes6 minutes
Process Mining3 minutes
Rodrigo Barnes from Aridhia14 minutes

1 readingTotal 10 minutes

IBM Watson10 minutes

1 assignmentTotal 30 minutes

Quiz 430 minutes

1 ungraded labTotal 60 minutes

Natural Language Processing Programming Task60 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 videosTotal 40 minutes

Introduction3 minutes
Graph Data & RDF7 minutes
Ontologies & Graph Data Conclusion5 minutes
Dr Steve Pavis8 minutes
Professor Mark Parsons8 minutes
Society, Law and Ethics6 minutes
Course Conclusion3 minutes

4 readingsTotal 40 minutes

SPARQL Querying10 minutes
General Data Protection Regulation (GDPR)10 minutes
Research Ethics10 minutes
Post-Course Survey10 minutes

1 assignmentTotal 30 minutes

Quiz 530 minutes

1 peer reviewTotal 120 minutes

Final Course Assessment120 minutes

1 ungraded labTotal 60 minutes

Graph Data Programming Task60 minutes

Instructors

Instructor ratings

(88 ratings)

Dr Areti Manataki

The University of Edinburgh

4 Courses688,747 learners

Dr Frances Wong

The University of Edinburgh

1 Course31,718 learners

Offered by

The University of Edinburgh

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Learner reviews

5 stars
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4 stars
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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 Apr 18, 2019

Excellent introduction to use of data science in healthcare with hands-on training in machine learning and NLP. Great course.

Reviewed on Apr 10, 2020

The course open a wide understanding over the use of Data science in Healthcare. This course is just for basic understanding. More you practice more you get.

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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.