Data Handling
Data handling is considered one of the most important topics in statistics as it deals with collecting sets of data, maintaining security, and the preservation of the research data. The data here is a set of numbers that help in analyzing that particular set or sets of data. Data handling can be represented visually in the form of graphs. Let us learn more about this interesting concept, the different graphs used, and solve a few examples for better understanding.
1.  Definition of Data Handling 
2.  Important Terms in Data Handling 
3.  Steps Involved in Data Handling 
4.  Graphical Representation of Data Handling 
5.  FAQs on Data Handling 
Definition of Data Handling
Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyze, make predictions and choices. Anything that can be grouped based on certain comparable parameters can be thought of as data. Parameters mean the context in which the comparison is made between the objects. Data handling usually represent in the form of pictographs, bar graphs, pie charts, histograms, line graphs, stem and leaf plots, etc. All of them have a different purpose to serve. Have a look at the composition of the air that we have learned about in our science classes.
The constituents of air are presented with different colors in the form of parts of a pie. Do you think, a bar chart, line graph, or any other graphical representation would be able to communicate the information as effectively as this one. Definitely no. With a detailed study of each of them, you can clearly understand the purpose of each of them and use them suitably.
Types of Data
Data handling is performed depending on the types of data. Data is classified into two types, such as Quantitative Data and Qualitative Data. Quantitive data gives numerical information, while qualitative data gives descriptive information about anything. Quantitative can be either discrete or continuous data.
Important Terms in Data Handling
In data handling, there are 4 important terms or most frequently used terms that make it simple to understand the concept better. The terms are:
 Data: It is the collection of numerical figures of any kind of information
 Raw Data: The observation gathered initially is called the raw data.
 Range: It is the difference between the highest and lowest values in the data collection.
 Statistics: It deals with the collection, representation, analysis, and interpretation of numerical data.
Steps Involved in Data Handling
Following are the steps to follow in data handling:
Steps  Details 
Purpose  The problem or purpose is identified and well defined 
Collection of Data  Data relevant to the purpose is collected. 
Presentation of Data  The collected data is to be presented in a form that is meaningful and easy to understand. It could be in the form of a simple table or tally marks etc. 
Graphical Representation of Data  Visual representation makes analysis and understanding of trends quicker and has a much greater impact. 
Analyzing the Data  The data undergoes inspection to derive useful and necessary information that helps in taking further actions. 
Conclusion/Inference  Here we provide a solution to our problem statement based on the analysis of the data. 
Graphical Representation of Data Handling
Data handling can be represented in a number of graphical ways. Here is a list of various types of graphical representations of data that are very effective in data handling.
Bar Graphs
Bar graphs represent data in the form of vertical or horizontal bars showing data with rectangular bars and the heights of bars are proportional to the values that they represent. Bar graphs help in the comparison of data and this type of graph is most widely used in statistics. Look at the image below as an example.
Pictographs or Picture Graphs
Pictograph is a type of graph where information is represented in the form of pictures, icons, or symbols. It is the simplest form of representing data in statistics and data handling. Since the use of images and symbols are more in a pictograph, interpreting data is made easy along with representing a large number of data. Look at the example below for a better understanding.
Line Graphs
In data handling the data represented in the form of a line on a graph is the line graph. The graph helps in showcasing the different trends or changes in the data. The line segment plotted on the graph is constructed by connecting individual data points together. Look at the example below to understand it better.
Pie Charts
A pie chart is data represented in a circular graph divided into smaller sectors to denote certain information. Pie charts help in showcasing the profit and loss for a business, while in school in showcasing the number depending on the data. This kind of chart is widely used in marketing sales. Look at the example below, the pie chart shows how people like the mentioned fruits from a group of 360.
Scatter Plot
Scatter plot represents the points and then the best fit line is drawn through some of the points. Any 3D data in data handling can be represented by a scatter plot. Look at the example below to understand it better.
Related Topic
Listed below are a few interesting topics related to data handling. Take a look.
Examples on Data Handling

Example 1: Henry wants to introduce his 5yearold daughter to data handling. Which type of graphical representation can he use for this?
Solution:
As his daughter is just 5 years old, he should prefer using Pictograph to introduce data handling. In this representation, simple pictures like circles, stars are drawn to represent different data.

Example 2: How is data represented graphically?
Solution: Various types of graphs that can be used for representing data are:
 Bar graph
 Scatter plot
 Line graph
 Area plot
 Pie chart/ Circle chart
 Picture graph
Depending on the purpose, a suitable graph can be chosen.

Example 3: Here is a review of an electronic product. Out of all the people who gave their reviews, 16 of them gave a 5star rating to the product. Can you find out how many people provided their feedback in all?
Solution:
Let the total reviews be x.
Number of people who gave 5 star = 16
Percentage of people who gave 5 star = 64%
So, number of people who gave 5 star = 64 % × x
16 = 64/100 × x
x = (16 × 64)/100
x = 25.
Therefore, 25 people gave reviews for the product
FAQs on Data Handling
What is Data Handling?
Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyze, make predictions and choices. There are two types of data handling namely quantitative data and qualitative data. Data handling can be represented through various graphs.
What are the Two Types of Data Handling?
The two types of data handling are qualitative data and quantitative data. Quantitive data gives numerical information, while qualitative data gives descriptive information about anything. Quantitative can be either discrete or continuous data.
What are the Steps Involved in Data Handling?
The six steps that are involved in data handling are:
 Purpose
 Collection of Data
 Presentation of Data
 Graphical Representation of Data
 Analyzing the Data
 Conclusion
What are the Types of Graphical Representations in Data Handling?
There are numerous types of graphical representation for the data that are available. Some of the most extensively used graphical representations are :
 Bar graph
 Scatter plot
 Line graph
 Area plot
 Pie chart/ Circle chart
What is the Difference Between Data and Information in Data Handling?
The term data refers to the collection of certain facts that are quantitive in nature like height, number of children, etc. Information on the other hand is a form of data after being processed, arranged, and presented in a form that gives meaning to the data.
What is the Difference Between the Chart and Graph?
The difference between chart and graph can be understood from the fact that  All graphs are charts but every chart is not a graph. Charts display data in the form of a diagram, table, or graph. So, the graph is just a pictorial way of presentation of information.