Exploratory Data Analysis using Data Visualization Techniques.
Published: 2023-10-30 13:44:41

Exploratory Data Analysis (EDA) refers to the method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables. EDA is normally carried out as a preliminary step before undertaking extra formal statistical analyses or modeling.

****Goals of EDA**
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1. Data Segmentation: EDA can contain dividing the information into significant segments based totally on sure standards or traits. This segmentation allows advantage insights into unique subgroups inside the information and might cause extra focused analysis.

2. Correlation and Relationships: EDA allows discover relationships and dependencies between variables. Techniques such as correlation analysis, scatter plots, and pass-tabulations offer insights into the power and direction of relationships between variables.

3. Data Cleaning: EDA involves examining the information for errors, lacking values, and inconsistencies. It includes techniques including records imputation, managing missing statistics, and figuring out and getting rid of outliers.
4. Descriptive Statistics: EDA utilizes precise records to recognize the important tendency, variability, and distribution of variables. Measures like suggest, median, mode, preferred deviation, range, and percentiles are usually used.

Examples of some data visualization techniques commonly used in EDA:

1. Histograms

Histograms display the distribution of a single numeric variable by dividing it into bins or intervals.

2. Box Plots (Box-and-Whisker Plots):
 
Box plots show the summary of a set of data, including minimum, first quartile, median, third quartile, and maximum. They are useful for identifying outliers and understanding the spread of the data.

3. Scatter Plots:

Scatter plots display the relationship between two continuous variables. Each data point is represented as a dot on the graph.

4. Pair Plots:

Pair plots (or scatterplot matrices) are used when dealing with multiple numeric variables. They display scatter plots for each pair of variables, and histograms for each variable on the diagonal.

5. Correlation Heatmaps:

Correlation heatmaps visually represent the correlation coefficients between different variables in a dataset. This is especially useful for understanding relationships between multiple variables.

6.Bar Charts:

Bar charts represent categorical data with rectangular bars. They are useful for comparing the frequency or distribution of different categories.

7.Pie Charts:

Pie charts represent the composition of a categorical variable as a circular graph. They are helpful for showing the relative proportions of different categories.
Line Charts:

**Data Visualization:**

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations.

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. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.

**General Types of Visualizations:
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1. Chart: Information presented in a tabular, graphical form with data displayed along two axes. Can be in the form of a graph, diagram, or map. 

2.Table: A set of figures displayed in rows and columns. 

3. Graph: A diagram of points, lines, segments, curves, or areas that represents certain variables in comparison to each other, usually along two axes at a right angle. 

4. Geospatial: A visualization that shows data in map form using different shapes and colors to show the relationship between pieces of data and specific locations. Learn more. 

5. Infographic: A combination of visuals and words that represent data. Usually uses charts or diagrams.

6. Dashboards: A collection of visualizations and data displayed in one place to help with analyzing and presenting data. Learn more.