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Algorithms for Data Science
Год издания: 2017
Автор: Steele, Brian, Chandler, John, Reddy, Swarna
Жанр или тематика: Python, R
Издательство: Springer International Publishing Switzerland
ISBN: ISBN 978-3-319-45797-0
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 438
Описание: This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

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Оглавление

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 What Is Data Science? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Diabetes in America. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Authors of the Federalist Papers . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Forecasting NASDAQ Stock Prices . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 The Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.7 Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.8 Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.9 R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.10 Terminology and Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.10.1 Matrices and Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.11 Book Website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Part I Data Reduction
2 Data Mapping and Data Dictionaries . . . . . . . . . . . . . . . . . . . . . 19
2.1 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Political Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4 Tutorial: Big Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5.1 Notation and Terminology . . . . . . . . . . . . . . . . . . . . . . . 28
2.5.2 The Political Contributions Example . . . . . . . . . . . . . . 29
2.5.3 Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6 Tutorial: Election Cycle Contributions . . . . . . . . . . . . . . . . . . . . 31
2.7 Similarity Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.7.1 Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.8 Tutorial: Computing Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.9 Concluding Remarks About Dictionaries . . . . . . . . . . . . . . . . . . 47
2.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.10.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.10.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 Scalable Algorithms and Associative Statistics . . . . . . . . . . . . 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Example: Obesity in the United States . . . . . . . . . . . . . . . . . . . 53
3.3 Associative Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4 Univariate Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4.1 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4.2 Histogram Construction . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.6 Tutorial: Histogram Construction . . . . . . . . . . . . . . . . . . . . . . . . 61
3.6.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.7 Multivariate Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.7.1 Notation and Terminology . . . . . . . . . . . . . . . . . . . . . . . 75
3.7.2 Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.7.3 The Augmented Moment Matrix. . . . . . . . . . . . . . . . . . 79
3.7.4 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.8 Tutorial: Computing the Correlation Matrix. . . . . . . . . . . . . . . 80
3.8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.9 Introduction to Linear Regression . . . . . . . . . . . . . . . . . . . . . . . 88
3.9.1 The Linear Regression Model. . . . . . . . . . . . . . . . . . . . . 89
3.9.2 The Estimator of β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.9.3 Accuracy Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.9.4 Computing R 2 adjusted . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.10 Tutorial: Computing β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.10.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.11.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.11.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4 Hadoop and MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.2 The Hadoop Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.2.1 The Hadoop Distributed File System . . . . . . . . . . . . . . 106
4.2.2 MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.2.3 Mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.2.4 Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.3 Developing a Hadoop Application . . . . . . . . . . . . . . . . . . . . . . . . 111
4.4 Medicare Payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.5 The Command Line Environment . . . . . . . . . . . . . . . . . . . . . . . . 113
4.6 Tutorial: Programming a MapReduce Algorithm . . . . . . . . . . . 113
4.6.1 The Mapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.6.2 The Reducer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.6.3 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.7 Tutorial: Using Amazon Web Services . . . . . . . . . . . . . . . . . . . . 124
4.7.1 Closing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
4.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
4.8.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
4.8.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Part II Extracting Information from Data
5 Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.2 Principles of Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.3 Making Good Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.3.1 Univariate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.3.2 Bivariate and Multivariate Data . . . . . . . . . . . . . . . . . . 142
5.4 Harnessing the Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5.4.1 Building Fig.5.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
5.4.2 Building Fig.5.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.4.3 Building Fig.5.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
5.4.4 Building Fig.5.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
5.4.5 Building Fig.5.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
5.4.6 Building Fig.5.10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
5.4.7 Building Fig.5.11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
5.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
6 Linear Regression Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.2 The Linear Regression Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.2.1 Example: Depression, Fatalism, and Simplicity . . . . . 164
6.2.2 Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.2.3 Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
6.2.4 Distributional Conditions . . . . . . . . . . . . . . . . . . . . . . . . 170
6.2.5 Hypothesis Testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
6.2.6 Cautionary Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
6.3 Introduction to R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
6.4 Tutorial: R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
6.4.1 Remark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 181
6.5 Tutorial: Large Data Sets and R . . . . . . . . . . . . . . . . . . . . . . . . . 181
6.6 Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
6.6.1 Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
6.6.2 The Extra Sums-of-Squares F-test . . . . . . . . . . . . . . . . 192
6.7 Tutorial: Bike Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
6.7.1 An Incongruous Result . . . . . . . . . . . . . . . . . . . . . . . . . . 200
6.8 Analysis of Residuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
6.8.1 Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
6.8.2 Example: The Bike Share Problem . . . . . . . . . . . . . . . . 202
6.8.3 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
6.9 Tutorial: Residual Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
6.9.1 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
6.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
6.10.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
6.10.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
7 Healthcare Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 217
7.2 The Behavioral Risk Factor Surveillance System . . . . . . . . . . . 219
7.2.1 Estimation of Prevalence . . . . . . . . . . . . . . . . . . . . . . . . 220
7.2.2 Estimation of Incidence. . . . . . . . . . . . . . . . . . . . . . . . . . 221
7.3 Tutorial: Diabetes Prevalence and Incidence . . . . . . . . . . . . . . . 222
7.4 Predicting At-Risk Individuals. . . . . . . . . . . . . . . . . . . . . . . . . . . 231
7.4.1 Sensitivity and Specificity. . . . . . . . . . . . . . . . . . . . . . . . 234
7.5 Tutorial: Identifying At-Risk Individuals . . . . . . . . . . . . . . . . . . 236
7.6 Unusual Demographic Attribute Vectors . . . . . . . . . . . . . . . . . . 243
7.7 Tutorial: Building Neighborhood Sets. . . . . . . . . . . . . . . . . . . . . 245
7.7.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
7.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
7.8.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
7.8.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
8 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.2 Hierarchical Agglomerative Clustering . . . . . . . . . . . . . . . . . . . . 254
8.3 Comparison of States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
8.4 Tutorial: Hierarchical Clustering of States. . . . . . . . . . . . . . . . . 258
8.4.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
8.5 The k-Means Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
8.6 Tutorial: The k-Means Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 268
8.6.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
8.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
8.7.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
8.7.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
Part III Predictive Analytics
9 k-Nearest Neighbor Prediction Functions . . . . . . . . . . . . . . . . . 279
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
9.1.1 The Prediction Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
9.2 Notation and Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
9.3 Distance Metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
9.4 The k-Nearest Neighbor Prediction Function . . . . . . . . . . . . . . 284
9.5 Exponentially Weighted k-Nearest Neighbors . . . . . . . . . . . . . . 286
9.6 Tutorial: Digit Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
9.6.1 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
9.7 Accuracy Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
9.7.1 Confusion Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
9.8 k-Nearest Neighbor Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 298
9.9 Forecasting the S&P 500. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
9.10 Tutorial: Forecasting by Pattern Recognition . . . . . . . . . . . . . . 300
9.10.1 Remark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
9.11 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
9.12 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
9.12.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
9.12.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
10 The Multinomial Naïve Bayes Prediction Function . . . . . . . . 313
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
10.2 The Federalist Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314
10.3 The Multinomial Naïve Bayes Prediction Function . . . . . . . . . 315
10.3.1 Posterior Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . 317
10.4 Tutorial: Reducing the Federalist Papers . . . . . . . . . . . . . . . . . . 319
10.4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
10.5 Tutorial: Predicting Authorship of the Disputed Federalist Papers . . . . . . . . 325
10.5.1 Remark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
10.6 Tutorial: Customer Segmentation . . . . . . . . . . . . . . . . . . . . . . . . 329
10.6.1 Additive Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
10.6.2 The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
10.6.3 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
10.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
10.7.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
10.7.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
11 Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
11.2 Tutorial: Working with Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
11.3 Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
11.3.1 Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
11.3.2 Estimation of the Mean and Variance . . . . . . . . . . . . . 350
11.3.3 Exponential Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . 352
11.3.4 Autocorrelation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
11.4 Tutorial: Computing ? ρ τ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
11.4.1 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359
11.5 Drift and Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359
11.6 Holt-Winters Exponential Forecasting . . . . . . . . . . . . . . . . . . . . 360
11.6.1 Forecasting Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362
11.7 Tutorial: Holt-Winters Forecasting . . . . . . . . . . . . . . . . . . . . . . . 363
11.8 Regression-Based Forecasting of Stock Prices . . . . . . . . . . . . . . 367
11.9 Tutorial: Regression-Based Forecasting . . . . . . . . . . . . . . . . . . . 368
11.9.1 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
11.10 Time-Varying Regression Estimators . . . . . . . . . . . . . . . . . . . . . 374
11.11 Tutorial: Time-Varying Regression Estimators . . . . . . . . . . . . . 375
11.11.1 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
11.12 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
11.12.1 Conceptual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
11.12.2 Computational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
12 Real-time Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
12.2 Forecasting with a NASDAQ Quotation Stream . . . . . . . . . . . 382
12.2.1 Forecasting Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 383
12.3 Tutorial: Forecasting the Apple Inc. Stream . . . . . . . . . . . . . . . 384
12.3.1 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
12.4 The Twitter Streaming API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
12.5 Tutorial: Tapping the Twitter Stream . . . . . . . . . . . . . . . . . . . . 391
12.5.1 Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
12.6 Sentiment Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
12.7 Tutorial: Sentiment Analysis of Hashtag Groups . . . . . . . . . . . 398
12.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
A Solutions to Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
B Accessing the Twitter API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
Доп. информация: Brian Steele is a full professor of Mathematics at the University of Montana and a Senior Data Scientist for SoftMath Consultants, LLC. Dr. Steele has published on the EM algorithm, exact bagging, the bootstrap, and numerous statistical applications. He teaches data analytics and statistics and consults on a wide variety of subjects related to data science and statistics.
John Chandler has worked at the forefront of marketing and data analysis since 1999. He has worked with Fortune 100 advertisers and scores of agencies, measuring the effectiveness of advertising and improving performance. Dr. Chandler joined the faculty at the University of Montana School of Business Administration as a Clinical Professor of Marketing in 2015 and teaches classes in advanced marketing analytics and data science. He is one of the founders and Chief Data Scientist for Ars Quanta, a Seattle-based data science consultancy.
Dr. Swarna Reddy is the founder, CEO, and a Senior Data Scientist for SoftMath Consultants, LLC and serves as a faculty affiliate with the Department of Mathematical Sciences at the University of Montana. Her area of expertise is computational mathematics and operations research. She is a published researcher and has developed computational solutions across a wide variety of areas spanning bioinformatics, cybersecurity, and business analytics.
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