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 fields Introduces the basic toolkit of
data analysis-including sampling, hypothesis testing, Bayesian
inference, regression, experiments, instrumental variables,
differences in differences, and regression discontinuity Uses
real-world examples and data from a wide variety of subjects
Includes practice questions and data exercises
General
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