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Statistical Application Development with R and Python（Second Edition）
最新章節：
Index
IfyouwanttohaveabriefunderstandingofthenatureofdataandperformadvancedstatisticalanalysisusingbothRandPython,thenthisbookiswhatyouneed.Nopriorknowledgeisrequired.Aspiringdatascientist,RuserstryingtolearnPythonandviceversa
目錄(80章)
倒序
- 封面
- 書名頁
- Statistical Application Development with R and Python - Second Edition
- Credits
- About the Author
- Acknowledgment
- 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. Data Characteristics
- Questionnaire and its components
- Experiments with uncertainty in computer science
- Installing and setting up R
- Using R packages
- Python installation and setup
- IDEs for R and Python
- The companion code bundle
- Discrete distributions
- Continuous distributions
- Summary
- Chapter 2. Import/Export Data
- Packages and settings – R and Python
- Understanding data.frame and other formats
- Using utils and the foreign packages
- Exporting data/graphs
- Pop quiz
- Summary
- Chapter 3. Data Visualization
- Packages and settings – R and Python
- Visualization techniques for categorical data
- Visualization techniques for continuous variable data
- Pareto chart
- A brief peek at ggplot2
- Summary
- Chapter 4. Exploratory Analysis
- Packages and settings – R and Python
- Essential summary statistics
- Techniques for exploratory analysis
- Summary
- Chapter 5. Statistical Inference
- Packages and settings – R and Python
- Maximum likelihood estimator
- Confidence intervals
- Hypothesis testing
- Summary
- Chapter 6. Linear Regression Analysis
- Packages and settings - R and Python
- The essence of regression
- The simple linear regression model
- Multiple linear regression model
- Regression diagnostics
- Model selection
- Summary
- Chapter 7. Logistic Regression Model
- Packages and settings – R and Python
- Model validation and diagnostics
- Logistic regression for the German credit screening dataset
- Summary
- Chapter 8. Regression Models with Regularization
- Packages and settings – R and Python
- Regression spline
- Ridge regression for linear models
- Summary
- Chapter 9. Classification and Regression Trees
- Packages and settings – R and Python
- Splitting the data
- Summary
- Chapter 10. CART and Beyond
- Packages and settings – R and Python
- Understanding bagging
- Summary
- Index 更新時間：2021-07-02 18:44:27
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