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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-statistics-data-analysis-public-health
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课程概述：欢迎参加《公共卫生统计学与数据分析导论》课程！本课程将教您统计分析的核心基础知识——变量类型、常见分布、假设检验等。同时，您将能够对未曾见过的数据集进行描述，了解其关键特征、优势与不足，进行基本的分析，并基于均值和比例形成和验证假设。此课程将为您提供扎实的基础，使您能继续学习更高级的分析技能并参加系列课程的后续教学。您将学习流行、灵活且完全免费的统计软件R，这是统计学和机器学习实践者普遍使用的工具。课程采用实践方式，您将首先通过媒体报道的医疗研究示例学习如何提出可检验的假设。接着，您将使用关于水果和蔬菜消费习惯的数据集进行实践分析，这些数据在现实中往往会比较杂乱，因为公共卫生数据集通常如此。课程中将有小测验及反馈，以帮助您检查理解程度，将提高您的批判性思维能力。在这个算法失控和假新闻泛滥的时代，这些技能显得尤为重要。 先修要求：课程提供一些公式以帮助理解，但您不需要数学学位即可顺利完成本课程。您只需具备基本的数字能力（例如，不会涉及微积分）和对结果的图形和表格呈现有些许了解。课程不要求具备R语言或编程知识。 课程大纲： 1. **公共卫生统计学导论**：统计学在公共卫生研究和实践中扮演着关键角色，您将通过两个实例开始学习：一个来自18世纪伦敦，另一个来自联合国。开展研究的首要任务是定义研究问题并将其表达为可检验的假设。通过媒体例子，您将了解哪些有效、哪些无效，并有机会从真实新闻故事中定义研究问题。 2. **变量类型、常见分布与抽样**：本模块将介绍统计分析中的一些核心知识：变量类型、常见分布与抽样。您将看到“良好行为”的数据分布（例如：正态分布与泊松分布）与公共卫生数据集中常见的真实数据分布之间的差异。 3. **R及RStudio导论**：现在是时候开始使用功能强大且完全免费的统计软件R及其流行界面RStudio。在水果和蔬菜消费的示例中，您将学习如何下载R、导入数据集并运行基本的描述性分析以了解变量。 4. **R中的假设检验**：在您学习如何定义研究问题和可检验的假设后，您将学习如何在R中应用假设检验并解读结果。由于所有医学知识均源自病人样本，随机和其他种类的变化意味着您在样本中测量的内容（例如平均体重指数）不一定反映整个群体的情况。在呈现时，将这种不确定性纳入平均BMI的估计中是至关重要的，这涉及到p值和置信区间的计算，这是统计分析中的基本概念。您将学习如何计算平均值和比例的p值和置信区间。
Name:Introduction to Statistics in Public Health
Description: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.
Name:Types of Variables, Common Distributions and Sampling
Description: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.
Name:Introduction to R and RStudio
Description: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.
Name:Hypothesis Testing in R
Description: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.
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.
