Data – What It Is, What We Can Do With It

所在平台: Coursera

课程主页: https://www.coursera.org/learn/data-what-it-is-what-can-we-do-with-it

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课程简介

课程名称：数据——它是什么，我们能做什么 课程概述：本课程旨在向学生介绍数据和统计学。通过学习，学生将能够解释描述性统计、因果分析和可视化，进而得出有意义的见解。课程首先提供一个统计分析目的的框架，讨论分析师如何利用数据进行描述性、因果性和预测性推断。随后，课程将涵盖如何为因果分析开发研究研究，计算和解释描述性统计，并设计有效的可视化图表。该课程帮助学员成为分析的批判性消费者。 如果你的工作领域越来越依赖数据驱动的决策，但你觉得自己无法解读和评估数据，这门课程将帮助你掌握数据素养的基本工具。 课程大纲： 第一部分：数据与理论 描述：许多人在考虑使用数据时，往往会直接考虑最佳的统计分析方法。然而，一个好的分析应始于强大的理论框架。理论不仅指导数据的收集、相应统计方法的选择、结果的解读，还决定了所需的研究设计，比如观察研究或实验。本模块将专注于开发高质量的理论，以指导描述性、因果性和预测性推断。 第二部分：因果框架 描述：确定因果关系通常是研究的主要动机。政策制定者希望了解实施新项目或其他政策工具将如何影响感兴趣的结果。比如，较小的班级规模是否会提高学生学习效果？严格的枪支购买者背景审查是否会减少枪支暴力？生物医学研究人员也希望了解新药物是否能改善某种疾病的结果。为了回答这些问题，分析师必须开发适合因果推断的研究设计。估计因果效应充满挑战，但理解政策、药物或其他干预措施的影响是至关重要的。 第三部分：描述性统计 描述：接下来的四节课中，我们将开始理解原始数据。仅仅看原始数据，例如电子表格，无法揭示关键信息。以调查问题“美国的歧视水平”为例，阅读原始数据无法反映平均受访者或不同回答选择之间的分布情况。为了更好地理解数据的分布特征，我们可以计算集中趋势度量、变异度量，并描述数据的离散情况。这些汇总统计允许研究人员对数据在现实世界中的意义进行简单而有力的初步推论。 第四部分：可视化 描述：世界知名的数据可视化专家爱德华·塔夫特曾说：“没有信息超载，只有糟糕的设计。”在传达分析结果时，尤其是试图说服观众时，一幅图像确实胜过千言万语。一个设计良好的图表可以利用少量或大量数据来构建具有说服力的论点。数据可视化突出了有关信息的特定要点，使观众能够得出在仅仅查看数字时几乎不可见的见解。总之，要善于用数据沟通，你必须掌握数据可视化的技能。

课程大纲

Part: 1

Title:Data and Theories

Description:When most people think about using data, they quickly jump to considering the best way to analyze it with statistical methods. A good analysis, however, begins with a strong theoretical framework. A good theory will guide the collection of data, selection of appropriate statistical methods and interpretation of the results. Further, the theory will determine what kind of research design is needed, such as an observational study or experiment. This module will focus on the development of high-quality theories that can be used to guide descriptive, causal and predictive inference.

Part: 2

Title:The Causality Framework

Description:Establishing causality is frequently the primary motivation for research. Policymakers often want to understand how the implementation of a new program or other policy tool will affect an outcome of interest. Will smaller class sizes increase student learning? Will the implementation of stricter background checks for gun buyers reduce gun violence? Biomedical researchers often want to understand whether a new medicine will improve a disease outcome. Will taking a drug improve life expectancy, or even cure the disease under study? To answer these and similar questions, analysts must develop research designs that are appropriate for causal inference. Estimating a causal effect is challenging, yet it is essential to understand the impacts of a policy, medicine or any other kind of intervention.

Part: 3

Title:Descriptive Statistics

Description:Over the next four lessons we'll begin to make sense of raw data. Staring at raw data, such as a spreadsheet, does not reveal much of anything about the key takeaway points. Consider a variable such as a survey question that asks about the level of discrimination in the U.S. (where the answer choices are "a lot," "some," "only a little," "none at all," and "don't know"). Reading the raw data does not tell you about the average respondent or the distribution of responses among the possible answer choices. To better understand the shape of the distribution, we can calculate measures of central tendency, measures of spread and characterize the data's dispersion. These summary statistics allow a researcher to draw some simple yet powerful initial conclusions about what the data tell us in a real-world sense.

Part: 4

Title:Visualizations

Description:Edward Tufte, a world-renowned expert of data visualization, once said, "There is no such thing as information overload. There is only bad design." When communicating the results of an analysis, and particularly when trying to persuade an audience, a picture is truly worth a thousand words. A well-designed graph can leverage either a small or large amount of data to make a convincing argument. Data visualizations highlight specific points about the underlying information and enable the viewer to draw insights that are nearly invisible when staring at the numbers alone. In short, to be a good at communicating with data, you must become skilled at visualizing data.

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