Java数据分析(影印版) 东南大学出版社 阿里云 kindle chm umd 极速 pdf txt 下载

数据分析是包含检查、清洗、转化和建模的整个过程，旨在发现有用的信息。Java是实现数据分析任务的流行语言之一。《Java数据分析（影印版·英文版）》将提供数据科学和相关流程步骤的快速概览。你将从中学到统计数据分析技巧，并通过流行的Java API和类库把它们实现。你还能在实际案例中学到诸如分类和回归之类的机器学习概念。在这个过程中，你将熟悉RapidMinet和Weka等工具，了解这些Java工具如何更有效地用于分析。还会学到如何与关系型、NoSQL和时间序列数据打交道。《Java数据分析（影印版·英文版）》也将介绍如何利用不同的Java类库创建富有洞见又容易理解的图表。学完《Java数据分析（影印版·英文版）》，你将对多种数据分析技巧和相应的Java实现拥有扎实的基础知识。

Preface

Chapter 1：Introduction to Data Analysis

Origins of data analysis

The scientific method

Actuarial science

Calculated by steam

A spectacular example

Herman Hollerith

ENIAC

VisiCalc

Data， information， and knowledge

Why Java?

Java Integrated Development Environments

Summary

Chapter 2：Data Pre_processing

Data types

Variables

Data points and datasets

Null values

Relational database tables

Key fields

Key-value pairs

Hash tables

File formats

Microsoft Excel data

XML and JSON data

Generating test datasets

Metadata

Data cleaning

Data scaling

Data filtering

Sorting

Merging

Hashing

Summary

Chapter 3：Data Visualization

Tables and graphs

Scatter plots

Line graphs

Bar charts

Histograms

Time series

Java implementation

Moving average

Data ranking

Frequency distributions

The normal distribution

A thought experiment

The exponential distribution

Java example

Summary

Chapter 4：Statistics

Deive statistics

Random sampling

Random variables

Probability distributions

Cumulative distributions

The binomial distribution

Multivariate distributions

Conditional probability

The independence of probabilistic events

Contingency tables

Bayes' theorem

Covariance and correlation

The standard normal distribution

The central limit theorem

Confidence intervals

Hypothesis testing

Summary

Chapter 5：Relational Databases

The relation data model

Relational databases

Foreign keys

Relational database design

Creating a database

SQL commands

Inserting data into the database

Database queries

SQL data types

JDBC

Using a JDBC PreparedStatement

Batch processing

Database views

Subqueries

Table indexes

Summary

Chapter 6：Regression Analysis

Linear regression

Linear regression in Excel

Computing the regression coefficients

Variation statistics

Java implementation of linear regression

Anscombe's quartet

Polynomial regression

Multiple linear regression

The Apache Commons implementation

Curve fitting

Summary

Chapter 7：Classification Analysis

Decision trees

What does entropy have to do with it?

The ID3 algorithm

Java Implementation of the ID3 algorithm

The Weka platform

The ARFF filetype for data

Java implementation with Weka

Bayesian classifiers

Java implementation with Weka

Support vector machine algorithms

Logistic regression

K-Nearest Neighbors

Fuzzy classification algorithms

Summary

Chapter 8：Cluster Analysis

Measuring distances

The curse of dimensionality

Hierarchical clustering

Weka implementation

K-means clustering

K-mecloids clustering

Affinity propagation clustering

Summary

Chapter 9：Recommender Systems

Utility matrices

Similarity measures

Cosine similarity

A simple recommender system

Amazon's item-to-item collaborative filtering recommender

Implementing user ratings

Large sparse matrices

Using random access files

The Netflix prize

Summary

Chapter 10：NoSQL Databases

The Map data structure

SQL versus NoSQL

The Mongo database system

The Library database

Java development with MongoDB

The MongoDB extension for geospatial databases

Indexing in MongoDB

Why NoSQL and why MongoDB?

Other NoSQL database systems

Summary

Chapter 11：Data Analysis with Java

Scaling， data striping， and sharding

Google's PageRank algorithm

Google's MapReduce framework

Some examples of MapReduce applications

The WordCount example

Scalability

Matrix multiplication with MapReduce

MapReduce in MongoDB

Apache Hadoop

Hadoop MapReduce

Summary

Appendix：Java Tools

The command line

Java

NetBeans

MySQL

MySQL Workbench

Accessing the MySQL database from NetBeans

The Apache Commons Math Library

The javax JSON Library

The Weka libraries

MongoDB

Index

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