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  • - Chapter 1: Introduction: What Is Data Science? ; - Chapter 2: Statistical Inference, Exploratory Data Analysis, and the Data Science Process; - Chapter 3: Algorithms; - Chapter 4: Spam Filters, Naive Bayes, and Wrangling; - Chapter 5: Logistic Regression; - Chapter 6: Time Stamps and Financial Modeling; - Chapter 7: Extracting Meaning from Data; - Chapter 8: Recommendation Engines: Building a User-Facing Data Product at Scale; - Chapter 9: Data Visualization and Fraud Detection; - Chapter 10: Social Networks and Data JournalismChapter 11: CausalityChapter 12: Epidemiology; - Chapter 13: Lessons Learned from Data Competitions: Data Leakage and Model Evaluation; - Chapter 14: Data Engineering: MapReduce, Pregel, and Hadoop; - Chapter 15: The Students Speak-; - Chapter 16: Next-Generation Data Scientists, Hubris, and Ethics (xsd:string)
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  • 2013 (xsd:gyear)
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  • 2013 (xsd:gyear)
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  • Englisch (EN) (xsd:string)
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  • 9781449358655 ()
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  • Doing data science (xsd:string)
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  • Buch (de)
  • Monographie (xsd:string)
  • book (en)
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  • Beijing u.a.: O'Reilly, 2013.- 375 S., graph. Darst. (xsd:string)
  • GESIS-BIB (xsd:string)
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