本課程是 Statistical Analysis with R for Public Health 專項課程 專項課程的一部分

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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.

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

建議：2-5 hours/week...

字幕：英語（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

立即開始，按照自己的計劃學習。

根據您的日程表重置截止日期。

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

建議：2-5 hours/week...

字幕：英語（English）

週

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 個視頻 （總計 23 分鐘）, 7 個閱讀材料, 2 個測驗

Uses of Statistics in Public Health5分鐘

Introduction to Sampling3分鐘

How to Formulate a Research Question3分鐘

Formulating a research question for the Parkinson's disease and supplement studies4分鐘

About Imperial College & the Team10分鐘

How to be successful in this course10分鐘

Grading policy10分鐘

Data set and Glossary10分鐘

Additional Reading10分鐘

John Snow and the Cholera outbreak of 184920分鐘

Instructions for Quiz10分鐘

Parkinson's Disease Study Issues15分鐘

Research Question Formulation20分鐘

週

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 個視頻 （總計 34 分鐘）, 3 個閱讀材料, 5 個測驗

Overview of types of variables4分鐘

Well-behaved Distributions7分鐘

Real-world Distributions and their Problems5分鐘

The Role of Sampling in Public Health Research8分鐘

How to choose a Sample4分鐘

Types of variables and the special case of age10分鐘

More on the 95% Confidence Interval10分鐘

Using your sample to estimate the population mean20分鐘

Types of variables20分鐘

Special case of age20分鐘

Well-behaved Distributions20分鐘

Ways of Dealing with Weird Data15分鐘

Sampling10分鐘

週

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 個視頻 （總計 20 分鐘）, 10 個閱讀材料, 2 個測驗

How to Load Data and run Basic Tabulations in R13分鐘

How to Calculate Percentiles10分鐘

Introduction to R20分鐘

R Resources10分鐘

Practice with R: Perform Descriptive Analysis10分鐘

Feedback: Descriptive Analysis10分鐘

How to judge visually if a variable is normally distributed in R10分鐘

Practice with R - trying it out for yourself10分鐘

Extra features in R10分鐘

Practice with R: Extra features10分鐘

Feedback: Extra features10分鐘

Distributions and Medians20分鐘

Calculations: Percentiles by Hand20分鐘

週

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 個視頻 （總計 20 分鐘）, 14 個閱讀材料, 5 個測驗

Hypothesis Testing6分鐘

Choosing the Sample Size for your Study4分鐘

Summary of Course2分鐘

The Coin Tossing Experiment: Part I10分鐘

The Coin Tossing Experiment: Part II10分鐘

The Coin Tossing Experiment: Feedback20分鐘

Degrees of Freedom 20分鐘

The chi-squared test with fruit and veg20分鐘

Feedback: Sample Size and Variation10分鐘

Comparing Two Means10分鐘

Practice with R: Hypothesis Testing10分鐘

Feedback: Hypothesis Testing in R10分鐘

The Difference between t-test and Chi-squared test10分鐘

Practice with R: Running a New Hypothesis Test10分鐘

P values and Thresholds10分鐘

Deaths data set for the end-of-course Assessment10分鐘

Final R code10分鐘

Hypothesis Testing10分鐘

The Coin Tossing Experiment: Evaluation30分鐘

Results: Running a New Hypothesis Test20分鐘

Hypothesis Testing15分鐘

End-of-course Assessment20分鐘

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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