数据科学（影印版） PDF下载

<；pre>；Preface
1. Introduction: What Is Data Science?
   Big Data and Data Science Hype
   Getting Past the Hype
   Why Now?
      Datafication
   The Current Landscape (with a Little History)
      Data Science lobs
   A Data Science Profile
   Thought Experiment: Meta-Definition
   OK, So What Is a Data Scientist, Really?
      In Academia
      In Industry
2. Statistical Inference, Exploratory Data Analysis, and the Data Science
   Process
   Statistic.a1 Thinking in the Age of Big Data
      Statistical Inference
      Populations and Samples
      Populations and Samples of Big Data
      Big Data Can Mean Big Assumptions
      Modeling
   Exploratory Data Analysis
      Philosophy of Exploratory Data Analysis
      Exercise: EDA
   The Data Science Process
      A Data Scientists Role in This Process
    Thought Experiment: How Would You Simulate Chaos?
    Case Study: RealDirect
      How Does RealDirect Make Money?
      Exercise: RealDirect Data Strategy
3. Algorithms
    Machine Learning Algorithms
    Three Basic Algorithms
      Linear Regression
      k-Nearest Neighbors (k-NN)
      k-means
    Exercise: Basic Machine Learning Algorithms
      Solutions
    Summing It All Up
    Thought Experiment: Automated Statistician
4. Spare Filters, Naive Bayes, and Wrangling
    Thought Experiment: Learning by Example
      Why Won	 Linear Regression Work for Filtering Spare?
      How About k-nearest Neighbors?
    Naive Bayes
      Bayes Law
      A Spare Filter for Individual Words
      A Spam Filter That Combines Words: Naive Bayes
    Fancy It Up: Laplace Smoothing
   Comparing Naive Bayes to k-NN
   Sample Code in bash
   Scraping the Web: APIs and Other Tools
   Jakes Exercise: Naive Bayes for Article Classification
      Sample R Code for Dealing with the NYT API
5. Logistic Regression
   Thought Experiments
   Classifiers
      Runtime
      You
      Interpretability
      Scalability
   M6D Logistic Regression Case Study
      Chck Models
      The Underlying Math

6．1ime Stamps and Financial Modeling 
7．Extracting Meaning from Data 
8．Recommendation Engines：Building a User-Facing Data Product at Scale
9．Data Visualization and Fraud Detection 
10．SociaI Networks and Data Journalism 
11．Causality 
12．Epidemiology 
13．Lessons Learned from Data Competitions：Data Leakage and Model Evaluation
14．Data Engineering：MapReduce，Pregel，and Hadoop 
15．The Students Speak
16．Next-Generation Data Scientists,Hubris，and Ethics 
Index<；/pre>；