SQL for Data Scientists

Index

• A
• Access (Microsoft), 2
• ad-hoc reporting, 143
• aggregation
  • AVG (average) function within, 91–93
  • CASE statement inside, 94–96
  • COUNT DISTINCT function within, 90–91
  • COUNT function within, 90–91
  • date functions within, 119–125
  • displaying group summaries, 80–83
  • exercise using, 244–245
  • granularity and, 138
  • GROUP BY within, 79–80
  • on joined tables, 86
  • LISTAGG for, 111
  • MAX function within, 88–90
  • MIN function within, 88–90
  • performing calculations inside, 84–88
  • underlying data prior to, 183
  • window functions and, 97, 103–108
• algorithms, 177, 185
• alias
  • WITH clause (CTE), 204–205
  • FROM clause and, 66
  • columns, 21–22, 65–66
  • within Exploratory Data Analysis (EDA), 130
  • of tables, 66, 89
• Amazon Redshift, 2, 115, 119
• analytical dataset
  • custom, 149–153
  • requirements of, 144–148
  • reusing of, 153–157
• AND NOT operator, 34, 38
• AND operator, 34, 37, 38
• AS keyword, 21, 22, 66, 149
• ASC (ascending) keyword, 18–19
• asterisk symbol, 16, 21
• attribute, 3–4
• averaging, 92
• AVG (average) function, 91–93, 97, 103–104

• B
• Batten, Dayne, 214
• BETWEEN keyword, 41–42
• binary classification algorithms, 185
• binary classification model, 173, 176–178
• binary flag field, 52–53, 177–178, 181, 190
• binning, 53–56
• blank, within database, 44

• C
• calculations
  • aggregate, 103
  • averaging, 92
  • date, 116
  • with DATEDIFF function, 123
  • of distance, 214
  • engineered features, 180
  • LAG and LEAD functions for, 111
  • performing inside aggregate functions, 84–88
  • ROUND function and, 92
  • rounding, 22–23
  • of sales,