Data Handling

Data handling is a widely used process of collecting, securing, and preserving the researched data. This data is a set of numbers and is generally depicted visually using graphs.

This visual representation of data helps students understand large information in simple terms. It enables to organize, analyze, and present information in a clear way, so people can easily understand and make decisions based on it. 

The process of data handling can be broken down into different steps. Let’s learn the steps that help us handle data effectively.

Data Collection

Data collection is the process of gathering information from different sources to analyze and use for decision-making. Since the collected data is unrefined, this process includes verifying that the information collected from different sources is correct. These sources might be large databases, internet platforms or surveys conducted by hand.

Data Preprocessing

Preprocessing data before use is essential, and it depends on how the obtained data will be used. This stage can be useful for data visualization and is mainly utilized for training a machine-learning model. In this step, we ensure that the data collected is structured, refined, and ready to apply. It includes the following steps:

• Data filtering
• Standardization and scaling
• Data conversion into a suitable format
• Managing disorganized data

Data Analysis

The most important step of data management is data analysis, which changes the complex data into simple insightful data. Depending on the particular use situation, this step changes. In general, it's the process of analyzing the data using a variety of tools to get the desired outcomes.

Data Presentation 

In the data presentation step, the collected data is converted into a well-structured and organized format. This provides a clear picture, as data is frequently sorted or occasionally combined into visualization techniques and saved in databases or spreadsheets. To ensure the data is ready for presentation, we use various analytical tools to maintain consistency and avoid redundancy.

We now understand data handling is a process involving several steps. Let’s now take a look at each of these steps:
 

Data handling can be visually represented in the form of graphs. The graphical representation of data enables students to easily interpret the objective of the presented data. The list of a few common graphical representations of data handling is given below:

Bar Graphs

Bar graphs present the collected data in rectangular bars that are either vertical or horizontal. In bar graphs, the height of each bar is equal to the values they indicate. We often use bar graphs to compare data. For a better understanding, look at the pictograph given below:
 

Pictographs 

Pictographs are also known as picture graphs where the data is expressed in the form of images, symbols, or icons. It is one of the most commonly used graphical representations in statistics and data processing. A pictograph enables students to understand the data in simple visual form. For a better understanding, look at the pictograph given below:
 

Line Graphs

Line graphs are formed by connecting the data points using a straight line. These are commonly used in displaying the change of a specific quantity over time. They can be used to represent the change of trends with time. Look at the sample image of a line graph below:

Pie Charts

Pie charts display data in a circular division of sections. They are often used to display a company’s profit and loss or to track marketing and sales. Look at an example below indicating the preferences of 360 individuals about fruits:
 

Scatter Plot

Scatter plots represent data points on a two-dimensional coordinate system. The values of two variables are represented by each point on the plot, enabling us to see any trends, patterns, or connections between them. We usually plot one variable on the horizontal axis (X-axis) and the other on the vertical axis.
 

Data handling is an important concept not only in education, but it is also widely used in various real-life situations. Let’s take a glance at how it applies in different fields:

• Data handling is used in schools to secure information about each student, track their performance, and analyze their needs.
  
   
• The government makes use of the relevant information about the people of a state to ensure their needs are met. It can also help in implementing policies for the public’s interest.
  
   
• Companies and businesses use data handling to analyze customer behavior and trends, which helps them improve their marketing strategy.
  
   
• Data handling is applied in weather forecasting to verify whether predictions are accurate and relevant.
  
   
• Hospitals use data handling to manage patient information, which helps in tracking their health conditions and providing better treatment.