Modern business analytics: practical data science for decision making
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
McGraw Hill
2022
|
| Ausgabe: | International student edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Klappentext |
| Beschreibung: | xxii, 442 Seiten Illustrationen, Diagramme |
| ISBN: | 9781264071678 9781266108334 1266108335 |
Internformat
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Datensatz im Suchindex
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| adam_text | CONTENTS About the Authors............................................................................v Preface............................................................................................ x What Is This Book About? x Guided Tour.................................................................................... xi Practical Data Science for Decision Making xi An Introductory Example xii Machine Learning xiv Computing with R xv Acknowledgments...................................................................... xvii 1 Regression...................................................................... 1 Linear Regression 3 Residuals 15 Logistic Regression 21 Likelihood and Deviance Time Series 30 Spatial Data 46 2 26 Uncertainty Quantification................................................ 55 Frequentisi Uncertainty 56 False Discovery Rate Control The Bootstrap 72 More on Bootstrap Sampling Bayesian Inference 91 3 67 86 Regularization and Selection........................................ 100 Out-of-Sample Performance 101 Building Candidate Models 108 Model Selection 130 Uncertainty Quantification for the Lasso 4 144 Classification.................................................................. 151 Nearest Neighbors 152 Probability, Cost, and Classification 158 Classification via Regression 160 Multinomial Logistic Regression 163 vii
Viii Contents 5 Causal Inference with Experiments............................. 175 Notation for Causal Inference 177 Randomized Controlled Trials 179 Regression Adjustment 184 Regression Discontinuity Designs 192 Instrumental Variables 199 Design of Experiments 208 6 Causal Inference with Controls....................................... 215 Conditional Ignorability 216 Double Machine Learning 224 Heterogeneous Treatment Effects 231 Using Time Series as Controls 241 7 Trees and Forests........................................................... 259 Decision Trees 261 Random Forests 268 Causal Inference with Random Forests 275 Distributed Computing for Random Forests 277 8 Factor Models................................................................ 282 Clustering 283 Factor Models and PCA 290 Factor Regression 302 Partial Least Squares 308 9 Text as Data.................................................................... 316 Tokenization 317 Text Regression 325 Topic Models 329 Word Embedding 338 10 DeepLearning................................................................ 345 The Ingredients of Deep Learning 346 Working with Deep Learning Frameworks Stochastic Gradient Descent 367 The State of the Art 374 Intelligent Automation 380 352
Contents Appendix: R Primer......................................................... 383 Getting Started with R 384 Working with Data 396 Advanced Topics for Functions 408 Organizing Code, Saving Work, and Creating Reports Bibliography 419 Glossary 424 Acronyms 433 Index 435 414 ¡X
S™ connect You have a lot going on, so we’ve designed Connect to fit your individual learning needs, making every minute you have to study more efficient and effective with our digital learning assistant. And when you download the free ReadAnywhere app to your smartphone or tablet, you can access your digital textbook anytime, anywhere—even if you’re offline. Get learning that fits your busy life with Connect. Students who access Connect sooner, do better.* Average increase in student scores when using Connect on Day 1 vs. Day 14 of class. 85% of students pass their courses using Connect compared to 72% of students not using Connect. Source: The Impact of Connect on Student Success. McGraw Hili Connect ՝ Effectiveness Study 2016
|
| adam_txt |
CONTENTS About the Authors.v Preface. x What Is This Book About? x Guided Tour. xi Practical Data Science for Decision Making xi An Introductory Example xii Machine Learning xiv Computing with R xv Acknowledgments. xvii 1 Regression. 1 Linear Regression 3 Residuals 15 Logistic Regression 21 Likelihood and Deviance Time Series 30 Spatial Data 46 2 26 Uncertainty Quantification. 55 Frequentisi Uncertainty 56 False Discovery Rate Control The Bootstrap 72 More on Bootstrap Sampling Bayesian Inference 91 3 67 86 Regularization and Selection. 100 Out-of-Sample Performance 101 Building Candidate Models 108 Model Selection 130 Uncertainty Quantification for the Lasso 4 144 Classification. 151 Nearest Neighbors 152 Probability, Cost, and Classification 158 Classification via Regression 160 Multinomial Logistic Regression 163 vii
Viii Contents 5 Causal Inference with Experiments. 175 Notation for Causal Inference 177 Randomized Controlled Trials 179 Regression Adjustment 184 Regression Discontinuity Designs 192 Instrumental Variables 199 Design of Experiments 208 6 Causal Inference with Controls. 215 Conditional Ignorability 216 Double Machine Learning 224 Heterogeneous Treatment Effects 231 Using Time Series as Controls 241 7 Trees and Forests. 259 Decision Trees 261 Random Forests 268 Causal Inference with Random Forests 275 Distributed Computing for Random Forests 277 8 Factor Models. 282 Clustering 283 Factor Models and PCA 290 Factor Regression 302 Partial Least Squares 308 9 Text as Data. 316 Tokenization 317 Text Regression 325 Topic Models 329 Word Embedding 338 10 DeepLearning. 345 The Ingredients of Deep Learning 346 Working with Deep Learning Frameworks Stochastic Gradient Descent 367 The State of the Art 374 Intelligent Automation 380 352
Contents Appendix: R Primer. 383 Getting Started with R 384 Working with Data 396 Advanced Topics for Functions 408 Organizing Code, Saving Work, and Creating Reports Bibliography 419 Glossary 424 Acronyms 433 Index 435 414 ¡X
S™ connect You have a lot going on, so we’ve designed Connect to fit your individual learning needs, making every minute you have to study more efficient and effective with our digital learning assistant. And when you download the free ReadAnywhere app to your smartphone or tablet, you can access your digital textbook anytime, anywhere—even if you’re offline. Get learning that fits your busy life with Connect. Students who access Connect sooner, do better.* Average increase in student scores when using Connect on Day 1 vs. Day 14 of class. 85% of students pass their courses using Connect compared to 72% of students not using Connect. 'Source: The Impact of Connect on Student Success. McGraw Hili Connect ՝ Effectiveness Study 2016 |
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| author | Taddy, Matt Hendrix, Leslie Harding, Matthew C. |
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| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | International student edition |
| format | Book |
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| isbn | 9781264071678 9781266108334 1266108335 |
| language | English |
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| spellingShingle | Taddy, Matt Hendrix, Leslie Harding, Matthew C. Modern business analytics practical data science for decision making Betriebsdaten (DE-588)4145038-3 gnd Datenanalyse (DE-588)4123037-1 gnd Unternehmensplanung (DE-588)4078609-2 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd |
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| title | Modern business analytics practical data science for decision making |
| title_auth | Modern business analytics practical data science for decision making |
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| title_full | Modern business analytics practical data science for decision making Matt Taddy, Leslie Hendrix, Matthew C. Harding |
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| title_full_unstemmed | Modern business analytics practical data science for decision making Matt Taddy, Leslie Hendrix, Matthew C. Harding |
| title_short | Modern business analytics |
| title_sort | modern business analytics practical data science for decision making |
| title_sub | practical data science for decision making |
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Inhaltsverzeichnis
Inhaltsverzeichnis
Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 ST 515 T121 |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |