DataVisualization

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DataVisualization

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Data Science Guide

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## Data Science Guide:
Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics mode.

CRISP-DM breaks the process of data mining into six major phases:

Business Understanding
    Data Understanding
    Data Preparation
    Modeling
    Evaluation
    Deployment

The sequence of the phases is not strict and moving back and forth between different phases as it is always required. 
==>The objective is to share a real guide to master the science of data from 0 to hero:

- Master Python for Data science
  
  - EDA
  
  - Modeling Machine learning Algorithms Using (Regression , Classification..)
  
  - NLP
  
  - Big Data Analytics tools(spark)
  
  - ......and many more 
  
# 1- Data visualization
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.
Common general types of data visualization:

Charts
    Tables
    Graphs
    Maps
    Infographics
    Dashboards

More specific examples of methods to visualize data:

Area Chart
    Bar Chart
    Box-and-whisker Plots
    Bubble Cloud
    Bullet Graph
    Cartogram
    Circle View
    Dot Distribution Map
    Gantt Chart
    Heat Map
    Highlight Table
    Histogram
    Matrix
    Network
    Polar Area
    Radial Tree
    Scatter Plot (2D or 3D)
    Streamgraph
    Text Tables
    Timeline
    Treemap
    Wedge Stack Graph
    Word Cloud
    And any mix-and-match combination in a dashboard!

DataVisualization

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