一本引人入胜的数据科学入门，强调批判性思维而非统计技术
数据科学或统计学导论不应该涉及证明复杂的定理或记忆晦涩的术语和公式，但这正是大多数定量导论教科书所强调的。相比之下，用数据清晰地思考首先侧重于批判性思维和概念理解，以便教会学生如何更好地消费和分析他们一生中会遇到的各种定量信息和论点。
除此之外，这本书还教授如何评估数据中观察到的关系是否反映了世界上的真实关系，如果是，是否是因果关系；如何在回答问题时进行信息量最大的比较；向使用定量证据进行辩论的其他人提出什么问题；哪些统计数据特别具有信息性或误导性；定量证据应该和不应该如何影响决策；以及如何利用道德价值观和数据做出更好的决策。书中充满了现实世界的例子，展示了它的思维工具如何应用于各种各样的主题，包括选举、国内冲突、犯罪、恐怖主义、金融危机、医疗保健、体育、音乐和太空旅行。
最重要的是，用数据清晰地思考表明，尽管我们的数据驱动时代有许多好处，但数据永远无法替代思考。
这是数据科学、统计学、政治学、经济学、心理学、社会学、公共政策、，和其他领域推出数据分析的基本工具包“•包括抽样、假设检验、贝叶斯推理、回归、实验、工具变量、差异差异和回归不连续性。使用来自各种主题的真实示例和数据，包括实践问题和数据练习
Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis by Ethan Bueno de Mesquita, Anthony Fowler
An engaging introduction to data science that emphasizes critical thinking over statistical techniques
An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.
Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel.
Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking.
An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fieldsIntroduces the basic toolkit of data analysis”•including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuityUses real-world examples and data from a wide variety of subjectsIncludes practice questions and data exercises
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