Template:Data Science: An Introduction/Navigation

 Data Science: An Introduction


 * Welcome to Data Science
 * 01: A History of Data Science
 * 02: A Mash-up of Disciplines
 * 03: Definitions of Data
 * 04: The Impact of Data Science
 * Thinking about the World
 * 05: Thinking Like a Visual Artist
 * 06: Thinking Like a Data Engineer
 * 07: Thinking Like a Hacker
 * 08: Thinking Like a Programmer
 * 09: Thinking Like a Scientist
 * 10: Thinking Like a Mathematician
 * 11: Thinking Like a Statistician
 * 12: Thinking Like a Domain Expert
 * Analyzing and Visualizing, Part One
 * 13: Single Variable Analysis
 * 14: Single Variable Tables and Plots
 * Setting up the Problem
 * 15: Theory-Based Inquiry
 * 16: Theoretical vs Measured Variables
 * 17: Hypothesis Testing
 * Collecting, Ingesting, Transforming Data
 * 18: Collecting vs Finding Data
 * 19: Data Dictionaries and Schemas
 * 20: Data Preparation and Metadata
 * Analyzing and Visualizing, Part Two
 * Scientific data visualisation
 * 21: Two Variable Analysis
 * 22: Two Variable Tables
 * 23: Two Variable Plots
 * Emergent Answers to Free Form Problems
 * 24: Non-Theory-Based Inquiry
 * 25: Exploratory Analysis
 * Analyzing and Visualizing, Part Three
 * 26: Scientific data visualisation
 * 26: Multiple Variable Analysis
 * 27: Multiple Variable Tables
 * 28: Multiple Variable Plots
 * Presenting Results
 * 29: Statistical Significance vs Substantive Significance
 * 30: Telling the Story
 * 31: Writing Good Data-Rich Paragraphs
 * 32: Creating Good Data-Rich Slides
 * Appendices
 * 01. Glossary
 * 02. Working in Groups
 * 03. 250 R Commands
 * 04. Research Design
 * 05. Data Dictionary

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