| 000 | nam a22 4500 | ||
|---|---|---|---|
| 999 |
_c32276 _d32276 |
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| 008 | 231001b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9780135258521 | ||
| 082 |
_a658.8342 _bROD |
||
| 100 | _aRodrigues, Joanne | ||
| 245 | _aProduct analytics : applied data science techniques for actionable consumer rights | ||
| 260 |
_bAddison Wesley, _c2021 _aBoston : |
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| 300 |
_axxii, 417 p. ; _bill., _c23 cm |
||
| 365 |
_b4122.18 _cINR _d01 |
||
| 490 | _aPearson business analytics series | ||
| 504 | _aIncludes bibliographical references and index. | ||
| 520 | _aProduct Analytics bridges the divide between high-value business insights and today’s best statistics and machine learning techniques, offering practical qualitative and quantitative techniques to generate actionable insight into customer behavior. Experienced data scientist and enterprise manager Joanne Rodrigues-Craig presents statistical techniques to determine why things happen, and how to change what people do at scale. She complements these with the social sciences’ most useful qualitative techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change. You’ll learn through intuitive examples from both web products and “real life,” including numeric examples illuminating hypothesis testing, regression, and other statistical techniques. Discover how to: Think like a social scientist to contextualize individual behavior in social environments, explore how human behavior develops, and establish the conditions for change Develop core metrics and effective KPIs for user analytics in any web product Understand statistical inference, the differences between correlation and causation and when to apply each technique Conduct more effective A/B tests Build intuitive predictive models to capture user behavior in product Using the latest quasi-experimental design techniques and statistical matching tease out causal effects from observational data Implement sophisticated targeting methods like uplift modeling for marketing campaigns Project business costs/subgroup population changes by using advanced demographic projection methods Do all this in R. | ||
| 650 | _aMachine learning | ||
| 650 | _aBig data | ||
| 650 | _aBusiness enterprises Data processing | ||
| 650 | _aConsumer Behavior | ||
| 650 | _aInformation visualization | ||
| 942 |
_2ddc _cBK |
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