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Measuring Disease in Epidemiology

This course is a part of Epidemiology for Public Health, a 3-course Specialization series from Coursera.

Epidemiological research is ubiquitous. Even if you don’t realise it, you come across epidemiological studies and the impact of their findings every single day. You have probably heard that obesity is increasing in high income countries or that malaria is killing millions of people in low income countries. It is common knowledge that smoking causes cancer and that physical activity is protective against heart disease. These facts may seem obvious today, but it took decades of epidemiological research to produce the necessary evidence. In this course, you will learn the fundamental tools of epidemiology which are essential to conduct such studies, starting with the measures used to describe the frequency of a disease or health-related condition. You will also learn how to quantify the strength of an association and discuss the distinction between association and causation. In the second half of the course, you will use this knowledge to describe different strategies for prevention, identify strengths and weaknesses of diagnostic tests and consider when a screening programme is appropriate.

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Imperial College London

Rating 4.5 based on 38 ratings
Length 5 weeks
Effort 4 weeks, 2-4 hrs/week
Starts Mar 23 (last week)
Cost $50
From Imperial College London via Coursera
Instructor Filippos Filippidis
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science
Tags Life Sciences Data Science Data Analysis Public Health

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What people are saying

According to other learners, here's what you need to know

easy to understand in 4 reviews

Easy to understand, very useful Quizzes are difficult, requiring far too much time.

I Although the videos are well done and the teacher makes it easy to understand, it is extremely frustrating that the answers are in decimals and there is no way to know exactly the answer the tests ask (even if you do it in excel, it won't necessarily give you the same as the professor has, depending on how he configured his excel program).

This isn't an easy subject, but it is made easy to understand.

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very good in 4 reviews

The course material, video lectures were very intractive teaching methodology which has enabled me to understand a lot, the course enabled me to have very good understanding about Epidemiological Study measures of disease frequencies.

it was wonderful, the exams were brain-stimulating Very informative The course is very good and directed to the subject that approaches with useful exercises to understand each concept very well.

very good A clear and concise framework about epidemiological measurements definition, meaning & applications, including measurements of associations, health impacts, and validity of screening tests.

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excellent course in 3 reviews

Very clear, lots of exercises An excellent course, very practical and professional.

Thank you for the excellent course materials, quizzes, and discussions.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Communications and Research Coordinator $49k

Junior Research Recruiter $62k

Assistant Faculty Research Analyst $63k

Research Associate - Asner Lab - Carnegie Spectranomics $63k

Adjunct Professor - Statistics and Research Methods $69k

Coordinator of Oncology Research $71k

Ph.D. Candidate in Industrial Engineering and Operations Research Manager $96k

Assistant Research and Data Analyst Consultant $104k

Technical Research and Development Engineer $109k

Research economist / Econometrician $117k

Research Plant Pathologist Lead $153k

Distinguished Associate Research Professor $289k

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Coursera

&

Imperial College London

Rating 4.5 based on 38 ratings
Length 5 weeks
Effort 4 weeks, 2-4 hrs/week
Starts Mar 23 (last week)
Cost $50
From Imperial College London via Coursera
Instructor Filippos Filippidis
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science
Tags Life Sciences Data Science Data Analysis Public Health

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