Public Health Analyst: Job Roles, Courses, and Salaries
December 6, 2024
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Master Statistics for Public Health and Learn R. Develop your statistical thinking skills and learn key data analysis methods through R
Instructors: Victoria Cornelius
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Beginner level
Familiarity with seeing graphs and tables. Basic numeracy (so NOT calculus, trigonometry etc). No medical, statistical or R knowledge is assumed.
(1,748 reviews)
Recommended experience
Beginner level
Familiarity with seeing graphs and tables. Basic numeracy (so NOT calculus, trigonometry etc). No medical, statistical or R knowledge is assumed.
Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice
Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software
Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R
Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results
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Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health.
In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around.
This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019.
The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data.
Applied Learning Project
In each course, you'll be introduced to key concepts and a data set to be used as a worked example throughout that course. Public health data are messy, with missing values and weird distributions all too common. The data you'll use are either real or simulated from real patient-level data sets (all anonymised and with usage permissions in place).
The emphasis will be on “learning through doing” and “learning through discovery” as you encounter typical data and analysis problems for you to solve and discuss among your fellow learners. You'll get the chance to work things out for yourself and with your peers before accessing the answers and explanation provided by the instructors.
Defend the critical role of statistics in modern public health research and practice
Describe a data set from scratch, including data item features and data quality issues, using descriptive statistics and graphical methods in R
Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R
Interpret the output from your analysis and appraise the role of chance and bias
Describe when a linear regression model is appropriate to use
Read in and check a data set's variables using the software R prior to undertaking a model analysis
Fit a multiple linear regression model with interactions, check model assumptions and interpret the output
Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software
Interpret the output from your analysis and appraise the role of chance and bias as potential explanations
Run multiple logistic regression analysis in R and interpret the output
Evaluate the model assumptions for multiple logistic regression in R
Run Kaplan-Meier plots and Cox regression in R and interpret the output
Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis
Describe and compare some common ways to choose a multiple regression model
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.
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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
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.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.
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.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
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.
Financial aid available,