Keerthika Jagadeesh — August 1, 2021
Beginner Data Exploration Data Visualization Python Structured Data

This article was published as a part of the Data Science Blogathon

DATA VISUALIZATION:

Data Visualization is one of the parts of descriptive analysis and it is the process of transferring the data into the visual context. It is the process of representing our data in a visual context such as histogram, bar chart, pie-chart, box-plot. It presents the data either in graphical or pictorial format. This process enables the user to understand the data with useful insights. The main goal of this process is to make the data easier to identify its patterns, trends and outliers.

Data Visualization is one of the important steps, it defines that after the information has been collected, it should be processed and modelled after these steps it should be visualized for conclusions to be made from the information.

WHY DATA VISUALIZATION IS IMPORTANT:

Data Visualization can be used in all kinds of business because it presents them in a graphical format, so humans can understand the data much in an easier manner. Data Visualization gives the simplest way to represent the information in a universal format by processing them in graphs and we can represent them in different kinds of visual format by making a slight adjustment. This process can help the business to identify the areas where we need to improve and we can also identify which factors affect customer satisfaction.

HOW DATA VISUALIZATION IS BEING USED:   

  • DECISION-MAKING: Data Visualization can also be used in the decision-making process. Now Businesses can recognize the various patterns of attributes more quickly so that they can interpret a large amount of data clearly and can get conclusions from that information. It is the fastest way to analyze the information.
  • EXAMINING THE MARKET: We can examine the market by giving all the information of different markets to the data visualization. It will give a clear insight into where should we focus more attention and where we are lagging and everything. We can get a clear picture of opportunities within the markets from the graphs and charts.
  • RELATIONSHIPS AND PATTERNS: Even the complicated data will start to make sense when it is represented graphically. Without data visualization, we cannot identify the correlation between the attributes. By identifying these relationships and their patterns can help the firms to get an idea of where to focus to reach the goals.
  • TRENDS: This one is the most important application of Data visualization. Without past and present data, we cannot make any predictions. Trends over time will show currently where we are and from this trend, we can also see that where we potentially go.

DATA VISUALIZATION TECHNIQUES: 

  • HISTOGRAM
  • CHARTS
  • PLOTS
  • MAPS AND GRAPHS
  • MATRICES

HISTOGRAMS: 

The histogram gives a graphical representation of our data which is mainly used for discrete or continuous data. A histogram graph has a set of bars where these bar groups those numbers into ranges. This graph represents the data by showing the number of data points that comes under within the specified range of values.

from matplotlib import pyplot as plt
import numpy as np
# Creating dataset
a = np.array([22, 87, 5, 43, 56,
              73, 55, 54, 11,
              20, 51, 5, 79, 31,
              27])
# Creating histogram
fig, ax = plt.subplots(figsize =(10, 7))
ax.hist(a, bins = [0, 25, 50, 75, 100])
# Show plot
plt.show()
Data Visualization | histogram

CHARTS:

Charts are the most basic and effective technique to show the data. For this, first, you have to choose the right chart. Charts are mainly used for representing the data’s time-series relationship. Some of the most common charts are:

  • PIE CHART
  • BAR CHART
  • LINE CHART

PIE CHART:  It is a circular statistical graph that has some slices where each slice represents the proportion of one particular element. Pie charts will be more useful if we have to explain the proportional composition of a certain attribute.

from matplotlib import pyplot as plt

import numpy as np

# Creating dataset

cars = ['AUDI', 'BMW', 'FORD',

        'TESLA', 'JAGUAR', 'MERCEDES']

data = [23, 17, 35, 29, 12, 41]

# Creating plot

fig = plt.figure(figsize =(10, 7))

plt.pie(data, labels = cars)

# show plot

plt.show()
piechart | Data Visualization

 


BAR CHART: It is used to compare the proportion of different categories. These categories are represented in bars either in vertical or in horizontal bars. In these bars, the height or length represent their values.

import matplotlib.pyplot as plt

# data to display on plots

x = [3, 1, 3, 12, 2, 4, 4]

y = [3, 2, 1, 4, 5, 6, 7]

# This will plot a simple bar chart

plt.bar(x, y)

# Title to the plot

plt.title("Bar Chart")

# Adding the legends

plt.legend(["bar"])

plt.show()
Bar chart | Data Visualization

 


LINE CHART: It is the simplest one of all the charts, this chart is used to plot the relationship between two variables. Each line plot defines that they are dependent on one variable to another.

import matplotlib.pyplot as plt

# data to display on plots

x = [3, 1, 3]

y = [3, 2, 1]

# This will plot a simple line chart

# with elements of x as x axis and y

# as y axis

plt.plot(x, y)

plt.title("Line Chart")

# Adding the legends

plt.legend(["Line"])

plt.show()
    Line chart 
        

 


PLOTS: 

Plots are the visual representation of displaying our two or more data sets in 2D or 3D to represent the relationship between these datasets and the parameter which is on the plot. Plots are used to display the relationships between two variables. Box plots, Scatter Plots and Bubble plots are the most used plots for data visualization. In Big data, more complex box plots are often used which helps people to understand the large data sets.

import matplotlib.pyplot as plt 

# data to display on plots 

x = [3, 1, 3, 12, 2, 4, 4]

y = [3, 2, 1, 4, 5, 6, 7]

   # This will plot a simple scatter chart

plt.scatter(x, y)

   # Adding legend to the plot

plt.legend("A")

   # Title to the plot

plt.title("Scatter chart")

plt.show()
scatter chart | Data Visualization

 


MAPS AND GRAPHS:

In many industries, maps are the popular method to present the data. In maps, they make the elements to locating on the relevant areas. Heat maps, Treemaps, dot distribution maps are the widely used maps. Geographical maps can also be used in organizations when your data is related geographically. Treemaps technique is best when if you want to present multiple categories.

MATRICES:

Matrix comes under the advanced data visualization technique. This technique helps to find the correlation between the multiple variables which are constantly updating.

WHICH TECHNIQUE IS BEST:

  • This part is one of the biggest challenges in business to choose which technique is best to represent our information.
  • First, you can go with the simpler charts, when you are exploring the new datasets.
  • After plotting one technique you can get an idea and based on the attributes you want to focus on, you can choose a technique according to that needs.
  • Before implementing a technique not only do you need to focus on the data but also you need to understand the goals. This step is more important before deciding the technique.

CONCLUSION:

From the above, we understand that how important is Data visualization in business, its benefits and their various techniques to make a visual format. In analytics, without this main step, we cannot process further steps. So, I conclude that Data visualization can be applied in any business and career. We also need data visualization because human brains cannot understand the whole big and raw data only by seeing. We must turn those data sets into a form, which could be easily understandable by us. We must need graphs and maps to identify their trends, relationships to gain insights and make a better conclusion.

 

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