Becoming a Data Head

Index

• A
• A/B tests/experiments, 17
• absolute value, 121
• accuracy, misunderstanding, 147–150
• adapting questions, 201
• affordability metric, 179
• AGI (artificial general intelligence), 187
• AI (artificial intelligence)
  • about, 187–188
  • big tech and, 188–189
• algorithmic bias, 198
• alternative hypothesis, 85–86
• Amazon, 168
• Ames Housing Data, 53
• ANI (artificial narrow intelligence), 187
• Apple, 168
• applications
  • of deep learning, 178–184
  • of supervised learning, 119
• arguing, with data
  • about, 37–38
  • missing data, 47–48
  • origin of data, 43–45
  • representativeness of data, 45–47
  • size of data, 48–49
  • "what would you do" scenario, 38–43
• artificial general intelligence (AGI), 187
• artificial intelligence (AI)
  • about, 187–188
  • big tech and, 188–189
• artificial narrow intelligence (ANI), 187
• asking questions, 22–23
• assuming
  • causality, 96–97
  • equivalence, 93
• assumptions, underlying in statistics, 32
• attributes. See columns
• audience, knowing your, 14
• average, 18

• B
• "bag of words" model, 153–157
• balancing decision errors, 88–89
• bar charts, 56–57
• Bayes' Theorem, 76–79
• Best, Joel (author)
  • Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, 32
• bias, in data
  • about, 32, 194
  • algorithmic bias, 198
  • confirmation bias, 197
  • bias, in data (Continued)effort bias (Sunk Cost Fallacy), 197
  • regression to the mean, 195
  • Simpson's Paradox, 195–197
  • survivorship bias, 194–195
  • uncategorized bias, 198–199
• Big Data, 48–49
• big tech
  • about, 168–169
  • artificial intelligence ...