도서 정보
도서 상세설명
1: A Conceptual Framework for Data Visualization
Data, information, knowledge, and insight
The transformation of data
Data visualization history
How does visualization help decision-making?
Visualization plots
Summary
2: Data Analysis and Visualization
Why does visualization require planning?
The Ebola example
A sports example
Creating interesting stories with data
Perception and presentation methods
Some best practices for visualization
Visualization tools in Python
Interactive visualization
Summary
3: Getting Started with the Python IDE
The IDE tools in Python
Visualization plots with Anaconda
Interactive visualization packages
Summary
4: Numerical Computing and Interactive Plotting
NumPy, SciPy, and MKL functions
Scalar selection
Slicing
Array indexing
Other data structures
Visualization using matplotlib
The visualization example in sports
Summary
5: Financial and Statistical Models
The deterministic model
The stochastic model
The threshold model
An overview of statistical and machine learning
Creating animated and interactive plots
Summary
6: Statistical and Machine Learning
Classification methods
Understanding linear regression
Linear regression
Decision tree
The Bayes theorem
The Naïve Bayes classifier
The Naïve Bayes classifier using TextBlob
Viewing positive sentiments using word clouds
k-nearest neighbors
Logistic regression
Support vector machines
Principal component analysis
k-means clustering
Summary
7: Bioinformatics, Genetics, and Network Models
Directed graphs and multigraphs
The clustering coefficient of graphs
Analysis of social networks
The planar graph test
The directed acyclic graph test
Maximum flow and minimum cut
A genetic programming example
Stochastic block models
Summary
8: Advanced Visualization
Computer simulation
Summary
appA: Appendix A: Go Forth and Explore Visualization
An overview of conda
Packages installed with Anaconda
Packages websites
About matplotlib
backindex: Appendix B: Index