数据科学（影印版） 下载 极速 rtf epub 在线 word txt pdf

　　现在人们已经意识到数据可以让选举或者商业模 式变得不同，数据科学作为一项职业正在不断发展。

但是你应该如何在这样一个广阔而又错综复杂的交叉 学科领域中开展工作呢？舒特、奥尼尔著的《数据科 学(影印版)》这本书将会告诉你所需要了解的一切。

它富有深刻见解，是根据哥伦比亚大学的数据科学课 程的讲义整理而成。

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 Scientist's 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't 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

Jake's 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

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