Python data visualization, data mining, machine learning, deep learning common libraries, IDES, etc.

First, the visualization method

1. Bar chart
2. Pie chart
3. Box-line Diagram (box chart)
4. Bubble chart
5. Histogram
6. Kernel density estimation (KDE) diagram
7. Line Surface Chart
8. Network Diagram
9. Scatter chart
10. Tree Chart
11. Violin chart
12. Square Chart
13. Three-dimensional diagram

Second, interactive tools

1. Ipython, Ipython Notebook
   
2. plotly

Iii. Python IDE Type

1. Pycharm, specifying a Java swing-based user interface
2. PyDev, SWT-based user interface (for Eclipse)
3. IEP (Interactive Editor for Pyhton), interactive editor
4. Canopy in Enthought: based on PYQT
5. Anaconda release of the Spyder in Continuum Analytics: PYQT-based

Iv. Interactive Visualization software package

1. D3.js
2. Bokeh
3. Vispy
4. Wakari
5. Ashiba

V. Statistics and data Mining algorithms

1. hmm-Hidden Markov model

   
   

2. viterbi-Viterbi algorithm

   
   

3. Logistic regression

   
   

4. Decision Tree: ID3, C4.5, CART---classification

   
   

5. K-means (k-means)---clustering algorithm

   
   

6. SVM Support Vector Machine---supervised learning, statistical classification and regression analysis

   
   

7. Maximum expectation (EM) algorithm

   
   

8. PageRank

   
   

9. AdaBoost

   
   

10. KNN (k-Neighbor algorithm)

    
    

11. Bayesian classifier, naive Bayesian

    
    

12. Random Forest

    
    

13. LDA Thematic model

    
    

Vi. Deep Learning

1. CNN convolutional Neural Network
   
2. RNN Cyclic Neural network

Python data visualization, data mining, machine learning, deep learning common libraries, IDES, etc.