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This course is part of Statistical Analysis with R for Public Health Specialization
Instructor: Alex Bottle
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(1,486 reviews)
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Beginner level
You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.
(1,486 reviews)
Recommended experience
Beginner level
You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.
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
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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.
Statistics has played a critical role of in public health research and practice, and you’ll start by looking at two examples: one from eighteenth century London and the other by the United Nations. The first task in carrying out a research study is to define the research question and express it as a testable hypothesis. With examples from the media, you’ll see what does and does not work in this regard, giving you a chance to define a research question from some real news stories.
5 videos7 readings2 assignments2 discussion prompts1 plugin
This module will introduce you to some of the key building blocks of knowledge in statistical analysis: types of variables, common distributions and sampling. You’ll see the difference between “well-behaved” data distributions, such as the normal and the Poisson, and real-world ones that are common in public health data sets.
6 videos3 readings5 assignments3 discussion prompts
Now it’s time to get started with the powerful and completely free statistical software R and its popular interface RStudio. With the example of fruit and vegetable consumption, you’ll learn how to download R, import the data set and run essential descriptive analyses to get to know the variables.
2 videos10 readings2 assignments1 discussion prompt
Having learned how to define a research question and testable hypothesis earlier in the course, you’ll learn how to apply hypothesis testing in R and interpret the result. As all medical knowledge is derived from a sample of patients, random and other kinds of variation mean that what you measure on that sample, such as the average body mass index, is not necessarily the same as in the population as a whole. It’s essential that you incorporate this uncertainty in your estimate of average BMI when presenting it. This involves the calculation of a p value and confidence interval, fundamental concepts in statistical analysis. You’ll see how to do this for averages and proportions.
4 videos14 readings5 assignments2 discussion prompts1 plugin
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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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Reviewed on Apr 4, 2021
This is a great introductory course to statistics. It's very clear, logical, and the progression of topics it's perfectly accompanied by examples and practical exercises.
Reviewed on Jan 4, 2021
The instructor videos are amazing- very relatable, applicable, and comprehensible! The principles are simplified as best as possible. This courses also good if you are very new to R.
Reviewed on May 27, 2020
Fantastic. Just what I needed as a doctor wanting to learn more about statistics. R is an amazing alternative to other programs such as STATA and SPSS.
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