Data Handling

Data handling is a widely used process for collecting, securing, and preserving data, whether complex or straightforward. Depending on the types of data, such as quantitative data, which is numerical information, or qualitative data, which is descriptive information, the process of data collection may vary.
This visual representation of data helps students interpret and analyze large amounts of information in simple terms. It enables us to organize, analyze, and present information clearly, so people can easily understand and make decisions based on it.

For example, a shopkeeper records the number of customers visiting his shop each day. Using this numerical data, he creates a line graph to interpret the data and identify which days have the highest and lowest sales.

Data handling is mainly classified into two major types:

• Quantitative data
  This type of data includes numbers that can be measured or counted. It tells us how much, how many, or how often, for example, marks scored by students.
   
• Qualitative data
  This data describes qualities, categories, or characteristics. It cannot be measured in numbers, for example, students' favorite colors.

In data handling, we work with both simple and complex data. These terms help us understand how information is organized, analyzed, and interpreted.

• Data: A collection of information represented using data points. It can include both quantitative (numerical) and qualitative (descriptive) data.
   
• Raw data: The initial information gathered during data collection, before arranging it into categories of data or studying the frequency of the data.
   
• Range: A measure used to understand how spread out the data is. It is calculated by subtracting the lowest value from the highest value in the dataset.
   
• Statistics: The branch that deals with collecting, organizing, and interpreting data to draw meaningful conclusions.

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 manually conducted surveys.

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 complex data into simple, insightful information. 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 data in a 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 changing trends over 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 fruit preferences of 360 individuals.

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.

Mastering data handling helps you organize, analyze, and interpret information effectively. These tips guide you to work accurately with different types of data in real-world scenarios.

 

• Learn to identify different types of data, such as numbers or descriptions, and whether they are countable or measurable.

• Arrange data neatly in tables, charts, or graphs to make it easier to understand.

• Learn to find the mean, median, mode, and range correctly.

• Practice reading data to identify patterns and understand what it reveals.

• Use real-life examples to improve your thinking and problem-solving skills.
  
   
• Parents can guide children in interpreting graphs from newspapers, weather apps, or bills to improve understanding.
  
   
• Teachers should help students interpret graphs and tables to draw conclusions and identify trends.
  
   
• Children should practice interpreting graphs to spot patterns, compare values, and draw simple conclusions.

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:

• Academics: Data handling is used in schools to secure information about each student, track their performance, and analyze their needs.
  
   
• Government: 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.
  
   
• Business: Companies use data handling to analyze customer behavior and trends, which helps them improve their marketing strategy.
  
   
• Weather Forecasting: Data handling is applied in weather forecasting to verify whether predictions are accurate and relevant.
  
   
• Hospitals: Data handling is used to manage patient information, which helps in tracking their health conditions and providing better treatment.