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Java Data Analysis
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IfyouareastudentorJavadeveloperorabuddingdatascientistwhowishestolearnthefundamentalsofdataanalysisandlearntoperformdataanalysiswithJava,thisbookisforyou.Somefamiliaritywithelementarystatisticsandrelationaldatabaseswillbehelpfulbutisnotmandatory,togetthemostoutofthisbook.AfirmunderstandingofJavaisrequired.
目錄(133章)
倒序
- 封面
- 書名頁
- Java Data Analysis
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- eBooks discount offers and more
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- 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 Preprocessing
- Data types
- Variables
- Data points and datasets
- Relational database tables
- Hash tables
- File formats
- Generating test datasets
- Summary
- Chapter 3. Data Visualization
- Tables and graphs
- Time series
- Java implementation
- Moving average
- Data ranking
- Frequency distributions
- The normal distribution
- The exponential distribution
- Java example
- Summary
- Chapter 4. Statistics
- Descriptive 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
- Summary
- Chapter 6. Regression Analysis
- Linear regression
- Polynomial regression
- Summary
- Chapter 7. Classification Analysis
- Decision trees
- Bayesian classifiers
- Logistic regression
- Summary
- Chapter 8. Cluster Analysis
- Measuring distances
- The curse of dimensionality
- Hierarchical 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. Big 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 A. 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 更新時間：2021-07-02 18:22:09
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