About this Specialization
26,125 recent views

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Familiarity with seeing graphs and tables. Basic numeracy (so NOT calculus, trigonometry etc). No medical, statistical or R knowledge is assumed.

Approx. 2 months to complete

Suggested 11 hours/week

English

Subtitles: English

What you will learn

  • Check

    Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice

  • Check

    Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software

  • Check

    Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R

  • Check

    Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results

Skills you will gain

Statistical ThinkingSurvival AnalysisLogistic RegressionData analysis with RLinear Regression

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Beginner Level

Familiarity with seeing graphs and tables. Basic numeracy (so NOT calculus, trigonometry etc). No medical, statistical or R knowledge is assumed.

Approx. 2 months to complete

Suggested 11 hours/week

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 4 Courses in this Specialization

Course1

Introduction to Statistics & Data Analysis in Public Health

4.7
83 ratings
19 reviews

Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.

...
Course2

Linear Regression in R for Public Health

4.8
38 ratings
7 reviews

Welcome to Linear Regression in R for Public Health! Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function. Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ. This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression. You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.

...
Course3

Logistic Regression in R for Public Health

4.6
25 ratings
3 reviews

Welcome to Logistic Regression in R for Public Health! Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. Additionally, the interpretation of the outputs from the regression model can differ depending on the perspective that you take, and public health doesn’t just take the perspective of an individual patient but must also consider the population angle. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too. By the end of this course, you will be able to: Explain when it is valid to use logistic regression Define odds and odds ratios Run simple and multiple logistic regression analysis in R and interpret the output Evaluate the model assumptions for multiple logistic regression in R Describe and compare some common ways to choose a multiple regression model This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation. If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health. We hope you enjoy the course!

...
Course4

Survival Analysis in R for Public Health

3.9
17 ratings
3 reviews

Welcome to Survival Analysis in R for Public Health! The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression. This one will show you how to run survival – or “time to event” – analysis, explaining what’s meant by familiar-sounding but deceptive terms like hazard and censoring, which have specific meanings in this context. Using the popular and completely free software R, you’ll learn how to take a data set from scratch, import it into R, run essential descriptive analyses to get to know the data’s features and quirks, and progress from Kaplan-Meier plots through to multiple Cox regression. You’ll use data simulated from real, messy patient-level data for patients admitted to hospital with heart failure and learn how to explore which factors predict their subsequent mortality. You’ll learn how to test model assumptions and fit to the data and some simple tricks to get round common problems that real public health data have. There will be mini-quizzes on the videos and the R exercises with feedback along the way to check your understanding. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. The three previous courses in the series explained concepts such as hypothesis testing, p values, confidence intervals, correlation and regression and showed how to install R and run basic commands. In this course, we will recap all these core ideas in brief, but if you are unfamiliar with them, then you may prefer to take the first course in particular, Statistical Thinking in Public Health, and perhaps also the second, on linear regression, before embarking on this one.

...

Instructors

Avatar

Alex Bottle

Reader in Medical Statistics
School of Public Health
Avatar

Victoria Cornelius

Senior Lecturer in Medical Statistics and Clinical Trials

Start working towards your Master's degree

This specialization is part of the 100% online Global Master of Public Health from Imperial College London. If you are admitted to the full program, your courses count towards your degree learning.

About Imperial College London

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • 3/4 hours a week for 3 to 4 months

  • The specialisation will assume no knowledge of statistics or R software.

  • We recommend taking the courses in the order in which they are displayed on the main page of the Specialization

More questions? Visit the Learner Help Center.