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Statistics for AIML - Data Visualization - Overview Tutorial

What is Data Visualization?

Data visualization is the representation of data or information in a graph, chart, or other visual format. It communicates the relationships of the data with images. This is important because it allows trends and patterns to be more easily seen.

What is its importance?

We need data visualization because a visual summary of information makes it easier to identify patterns and trends than looking through thousands of rows on a spreadsheet. It’s the way the human brain works. 

Since the purpose of data analysis is to gain insights, data is much more valuable when it is visualized. 

Even if a data analyst can pull insights from data without visualization, it will be more difficult to communicate the meaning without visualization. 

Charts and graphs make communicating data findings easier even if you can identify the patterns without them

 

Common Type Of Chart Use for Data Visualization-

 

Line Chart

A line chart is, as one can imagine, a line or multiple lines showing how single, or multiple variables develop over time. 

 

PieChart

A pie chart is a circular graph divided into slices. The larger a slice is the bigger portion of the total quantity it represents.

 

Bar Graph

A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Can be of one variable or many variables.

 

Histogram

A series of bins showing us the frequency of observations of a given variable.

 

Scatterplot

A scatter plot is a great indicator that allows us to see whether there is a pattern to be found between two variables. E.g.: Positive or negative relationship.

Statistics for AIML

Statistics for AIML

  • Introduction
  • Data Visualization
    • Overview
  • Descriptive statistics
    • Overview
    • Calculate Z Score
    • Covariance and Covariance matrix
    • Covariance vs. Correlation
    • QQ-Plot
    • Central Limit Theorem
  • Inferential Statistics
    • Overview
    • Hypothesis Testing
    • Statistical Test and there types
    • Bias Variance Trade Off
  • Regression Metrics
    • Overview
    • Accuracy
    • PR Curve (Precision-Recall Curve)
    • AUC-ROC Curve
    • Different types of Sampling
    • Skewness
    • Kurtosis
    • Degree Of freedom
    • Different Types Of Probability Distribution
    • Outlier
    • Bayes Theorem
    • Probability

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