Este curso forma parte de Programa Especializado - Statistical Analysis with R for Public Health

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Programa Especializado - Statistical Analysis with R for Public Health

Imperial College London

Acerca de este Curso

4.5

2 calificaciones

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.

Comienza de inmediato y aprende a tu propio ritmo.

Restablece las fechas límite en función de tus horarios.

You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.

Sugerido: 2-5 hours/week...

Subtítulos: Inglés (English)

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

Run basic analyses in RR ProgrammingUnderstand common data distributions and types of variablesFormulate a scientific hypothesis

Comienza de inmediato y aprende a tu propio ritmo.

Restablece las fechas límite en función de tus horarios.

You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.

Sugerido: 2-5 hours/week...

Subtítulos: Inglés (English)

Semana

1Statistics has played a critical role of statistics 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 videos (Total 23 minutos), 7 readings, 2 quizzes

Uses of Statistics in Public Health5m

Introduction to Sampling3m

How to Formulate a Research Question3m

Formulating a research question for the Parkinson's disease and supplement studies4m

About Imperial College & the Team10m

How to be successful in this course10m

Grading policy10m

Data set and Glossary10m

Additional Reading10m

John Snow and the Cholera outbreak of 184920m

Instructions for Quiz10m

Parkinson's Disease Study Issues15m

Research Question Formulation20m

Semana

2This 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 videos (Total 34 minutos), 3 readings, 5 quizzes

Overview of types of variables4m

Well-behaved Distributions7m

Real-world Distributions and their Problems5m

The Role of Sampling in Public Health Research8m

How to choose a Sample4m

Types of variables and the special case of age10m

More on the 95% Confidence Interval10m

Using your sample to estimate the population mean20m

Types of variables20m

Special case of age20m

Well-behaved Distributions20m

Ways of Dealing with Weird Data15m

Sampling10m

Semana

3Now 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 videos (Total 20 minutos), 10 readings, 2 quizzes

How to Load Data and run Basic Tabulations in R13m

How to Calculate Percentiles10m

Introduction to R20m

R Resources10m

Practice with R: Perform Descriptive Analysis10m

Feedback: Descriptive Analysis10m

How to judge visually if a variable is normally distributed in R10m

Practice with R - trying it out for yourself10m

Extra features in R10m

Practice with R: Extra features10m

Feedback: Extra features10m

Distributions and Medians20m

Calculations: Percentiles by Hand20m

Semana

4Having 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 videos (Total 20 minutos), 14 readings, 5 quizzes

Hypothesis Testing6m

Choosing the Sample Size for your Study4m

Summary of Course2m

The Coin Tossing Experiment: Part I10m

The Coin Tossing Experiment: Part II10m

The Coin Tossing Experiment: Feedback20m

Degrees of Freedom 20m

The chi-squared test with fruit and veg20m

Feedback: Sample Size and Variation10m

Comparing Two Means10m

Practice with R: Hypothesis Testing10m

Feedback: Hypothesis Testing in R10m

The Difference between t-test and Chi-squared test10m

Practice with R: Running a New Hypothesis Test10m

P values and Thresholds10m

Deaths data set for the end-of-course Assessment10m

Final R code10m

Hypothesis Testing10m

The Coin Tossing Experiment: Evaluation30m

Results: Running a New Hypothesis Test20m

Hypothesis Testing15m

End-of-course Assessment20m

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....

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....

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