# Visualize to Realize your Data

## A need for Quality Data Visualization is as high as ever!

By now, if you have experienced the perks of being a typical Data Analyst and Machine Learning Enthusiast, you would just love exploring with the data and the storyline behind everything you code!

Being a Data Scientist, you are not only a Computer Engineer or Programmer, you are just an all rounder! Though it’s seeming an exaggeration, you are hearing the fact! Being in this field, you are actually exploring the changing world!

While it is the need of the hour for analysis, it is important to give you a presentable view of what people have to conveyed. Not everyone are acquainted with data and its analysis, and so, this is the time where the famous saying** “A picture conveys thousand words”** comes into picture.

Today let us dive into some important attributes of Data Visualization.

From our grade 1, we have been looking into Data Handling! Ofcourse, it used to be in the top list of my favorite courses, as Data Handling is just to easy :D

But, after being an actual practitioner of Data Handling, now I realize the how crucial each of the Data Visualization tool each one holds for!

Let us now see different representations of the Data and its usage.

# Line Plot

**Line charts** are best to show trends ** over a period of time,** and multiple lines can be used to show trends in more than one group.

# Bar Plot

**Bar charts** are useful for ** comparing quantities **corresponding to different groups.

# Heatmaps

**Heatmaps** can be used to find ** color-coded patterns** in tables of numbers.

**Scatter plots**

**Scatter plots** show the relationship between two** continuous variables**; if color-coded, we can also show the relationship with a third categorical variable

**Regression line**

Including a **regression line** in the scatter plot makes it easier to see any linear relationship between two variables.

# Swarm Plot

**Categorical scatter plots** show the relationship between a continuous variable and a categorical variable.

**Histograms**

**Histograms** show the distribution of a single numerical variable.

**KDE plots**

**KDE plots** (or **2D KDE plots**) show an estimated, smooth distribution of a single numerical variable (or two numerical variables).

This is the basic view of the different visualizations we use according to the scenario.