Data Handling

Data Handling – Definition, Types, Examples | Graphical Representation of Data

Our life is all surrounded by numbers likewise marks scored, making a note of the height, weight, runs made, etc. All these are nothing but data and this article explains all about what is meant by data handling, how to organize data in the form of graphs, charts, types of data handling, calculation of mean, median, mode, etc. Practice the different questions available for representing various data in the form of bar graphs, pictographs, histograms, line graphs, etc., and get a good hold of the topics.

List of Data Handling Topics

Simply click on the links available below to learn in-depth about the respective topic.

What is Data Handling?

Data Handling is the process of collecting data and representing it in different forms. It is Securing the Research Data Gathered, Archived, Disposed of in a safe way once the analysis process is completed. You can use the data for comparing, taking out mean, median, mode, etc. The information which is collected initially is known as raw data and it can be in any form be it words, measurements, numbers, or descriptions.

Types of Data

Data Handling can be performed depending on the type of data. In general, the data is classified into two types namely

  • Qualitative Data
  • Quantitative Data

Qualitative Data: It gives descriptive information about a certain thing.

Quantitative Data: It provides numerical information and is further classified into two types namely discrete data, continuous data. Discrete Data takes values like whole numbers whereas continuous data takes a certain range.

How to Represent Data Handling?

Data can be denoted in the following ways and they are as such

  • Bar Graph
  • Pictograph
  • Line Graph
  • Stem and Leaf Plots
  • Histogram
  • Dot Plots
  • Cumulative Tables and graphs
  • Frequency Distribution

Steps Involved in Data Handling

Steps Explanation
Purpose Firstly identify the purpose
Collection of Data Collect the data relevant to the purpose
Presentation of Data Represent the collected data in the form of a meaningful and easy-to-understand way be it in the form of a table, tally marks, etc.
Graphical Representation of Data Visual Representation of Data can be quite easier for analysis as well as for understanding and has a greater impact.
Analyzing Data Look for ways to derive useful information so that you can proceed further
Conclusion Based on the analysis we did draw a conclusion to the given problem

Problems on Data Handling

Example 1.
Below is the table that tells us the number of chocolates present in different boxes. Draw a Pictograph for the given information?

Boxes Number of chocolates
Box 1 21
Box 2 14
Box 3 42
Box 4 7


We have considered each chocolate symbol as 7 chocolates and interpreted the above data in pictorial way for better visualization.

Boxes Number of chocolates
Box 1 chocolatechocolatechocolate
Box 2 chocolatechocolate
Box 3 chocolatechocolatechocolatechocolatechocolatechocolate
Box 4 chocolate

Example 2.

Students are given a pictograph describing the favorite ice cream of children. Based on the information given answer the questions asked?

worksheet on pictograph and bar graph example 1

1. How many students voted for the vanilla?
2. What flavor did the students like the most?
3. What flavor did students like the least?
4. How many students voted for chocolate than vanila?
1. No. of votes students cast for vanilla = 7
2. The flavor students liked the most is chocolate chips
3. The flavor students liked the least is strawberry
4. No. of students voted for chocolate than vanilla is 1.

Example 3.

The Sales of 3 Laptop company’s in the three consecutive months is represented by the table. Draw a Bar Graph.

Lenovo Hp Dell
Jan 200 300 400
Feb 500 400 600
Mar 600 500 700
Apr 800 700 900

worksheet on pictograph and bargraph example 2

Example 4.

The below pie chart describes how often 100 people use different modes of transport. Answer the Questions on the below diagram?

i) What percentage of people use the train most often?
ii) How many people use trains most often?
iii) How many people use buses most often?


i) 10% of people use the trains most often
ii) We need to find how many people use cars
No. of People given = 100
40% of people use cars
thus \(\frac { 40 }{ 100 } \)*100
= 40
Therefore, 40 people use cars most often
iii) No. of people = 100
Perecentage of People using buses = 20%
thus \(\frac { 20 }{ 100 } \)*100 = 20
Therefore, 20 people uses buses often.

Example 5.
The numbers of newspapers sold at a local shop over the 6 days are: 24, 20, 16, 16, 28, 32. State the frequency?
Given that,
The total number of newspapers sold at the local shop past 6 days are 24, 20, 16, 16, 28, 32
By arranging the newspapers numbers in ascending order we get 16, 16, 20, 24, 28, 32
The frequency table for the papers sold is given here:

16 2
20 1
24 1
28 1
32 1
Total 6

Example 6.
Find the mean, median, mode, and range for the following list of values 5, 11, 7, 7, 1, 8, 2, 6, and 13?
Mean= 5+11+7+7+1+8+2+6+13=66 ; 60/8= 7.33
Median= 7; the middle number when we arrange numbers in Ascending or Descending : 1,2,5,6,7,7,8,11,13
Mode=7; the number that occurs most often
Range=13-1=12; the difference between the highest and the lowest values in the data set.

FAQs on Data Handling

1. What is Data Handling in Simple Words?

Data Handling is the process of collecting data and representing it in different forms. It is sometimes referred to as Statistics.

2. What are the two types of data handling?

There are two types of data handling namely qualitative data, quantitative data.

3. How to find Mean in Data Handling?

Mean is the average of a set of data. We can find the mean by simply adding all the numbers in the data set and then dividing the sum by the number of values in the data set.

4. What is meant by Class Size in Data Handling?

Class Size refers to the range i.e. the difference between the upper limit and lower limit.

5. How is Data Represented Graphically?

We can represent data graphically in various forms such as

  • Bar Graph
  • Scatter Plot
  • Line Graph
  • Area Plot
  • Pie Chart/ Circle Chart
  • Picture Graph

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