基本信息

书名：Java数据分析(影印版)

定价：94.00元

作者： 约翰·R.哈伯德(John R.Hubbard)

出版社：东南大学出版社

出版日期：2018-09-01

ISBN：9787564177362

字数：

页码：

版次：1

装帧：平装-胶订

开本：16开

商品重量：0.4kg

编辑

内容提要

数据分析是包检查、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RapidMi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。

目录

PrefaceChapter 1： Introdu*ion to D*a Analysis
Origins of d*a analysis
The scientific method
A*uarial science
Calcul*ed by steam
A spe*acular example
Her*n Hollerith
ENIAC
VisiCalc
D*a， infor*tion， and knowledge
Why Java
Java Integr*ed Development Environments
Sum*ryChapter 2： D*a Pre_processing
D*a types
Variables
D*a points and d*asets
Null values
Rel*ional d*abase tables
Key fields
Key-value pai*
Hash tables
File for*ts
Microsoft Excel d*a
XML and JSON d*a
Gener*ing test d*asets
Metad*a
D*a cleaning
D*a scaling
D*a filtering
Sorting
Merging
Hashing
Sum*ryChapter 3： D*a Visualiz*ion
Tables and graphs
Sc*ter plots
Line graphs
Bar charts
Histograms
Time series
Java implement*ion
Moving average
D*a ranking
Frequency distributions
The nor*l distribution
A thought experiment
The exponential distribution
Java example
Sum*ryChapter 4： St*istics
Descriptive st*istics
Random sampling
Random variables
Probability distributions
Cu*l*ive distributions
The binomial distribution
Multivari*e distributions
Conditional probability
The independence of probabilistic events
Contingency tables
Bayes' theorem
Covariance and correl*ion
The standard nor*l distribution
The central limit theorem
Confidence intervals
Hypothesis testing
Sum*ryChapter 5： Rel*ional D*abases
The rel*ion d*a model
Rel*ional d*abases
Foreign keys
Rel*ional d*abase design
Cre*ing a d*abase
SQL *nds
Inserting d*a into the d*abase
D*abase queries
SQL d*a types
BC
Using a BC PreparedSt*ement
B*ch processing
D*abase views
Subqueries
Table indexes
Sum*ryChapter 6： Regression Analysis
Linear regression
Linear regression in Excel
Computing the regression coefficients
Vari*ion st*istics
Java implement*ion of linear regression
Ansbe's quartet
Polynomial regression
Multiple linear regression
The Apache Commons implement*ion
Curve fitting
Sum*ryChapter 7： Classific*ion Analysis
Decision trees
Wh* does entropy have to do with it
The ID3 algorit*
Java Implement*ion of the ID3 algorit*
The Weka pl*form
The ARFF filetype for d*a
Java implement*ion with Weka
Bayesian classifie*
Java implement*ion with Weka
Support ve*or *chine algorit*s
Logistic regression
K-Nearest Neio*
Fuzzy classific*ion algorit*s
Sum*ryChapter 8： Cluster Analysis
Measuring distances
The cu*e of dimensionality
Hierarchical clustering
Weka implement*ion
K-means clustering
K-mecloids clustering
Affinity propag*ion clustering
Sum*ryChapter 9： Remender Systems
Utility *trices
Similarity measures
Cosine similarity
A simple remender system
Am*on's item-to-item collabor*ive filtering remender
Implementing user r*ings
Large spa*e *trices
Using random access files
The Netflix prize
Sum*ryChapter 10： NoSQL D*abases
The Map d*a stru*ure
SQL ve*us NoSQL
The Mongo d*abase system
The Library d*abase
Java development with MongoDB
The MongoDB extension for geosp*ial d*abases
Indeng in MongoDB
Why NoSQL and why MongoDB
Other NoSQL d*abase systems
Sum*ryChapter 11：D*a Analysis with Java
Scaling， d*a striping， and sharding
Google's PageRank algorit*
Google's MapReduce framework
Some examples of MapReduce applic*ions
The WordCount example
Scalability
M*rix *ltiplic*ion with MapReduce
MapReduce in MongoDB
Apache Hadoop
Hadoop MapReduce
Sum*ry
Appendix： Java Tools
The *nd line
Java
NetBeans
MySQL
MySQL Workbench
Accessing the MySQL d*abase from NetBeans
The Apache Commons M*h Library
The *avax JSON Library
The Weka libraries
MongoDBIndex

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