What is Data ? 

Introduction

In the field of computing, data refers to information that has been transformed into a format suitable for efficient movement and processing. In today’s computer systems and communication channels, data is typically converted into binary digital form. It can be used both as a singular and plural subject. The term “raw data” is used to describe information in its most fundamental digital form.

What is Data ? 

The idea of data in computing originated from the groundbreaking work of Claude Shannon, an influential American mathematician often referred to as the father of information theory. He introduced binary digital concepts by applying two value Boolean logic to electronic circuits. Binary digit formats form the foundation of CPUs, semiconductor memories, disk drives and many peripheral devices commonly used in modern computing. Initially, computer input relied on methods like punch cards, followed by magnetic tape and hard disks.

As business computing evolved, the significance of data became increasingly apparent through terms like “data processing” and “electronic data processing,” which were widely used at that time to encompass what we now recognize as information technology. Over time, specialization occurred within corporate computing leading to the emergence of a distinct profession focused on managing and processing data alongside the growth of corporate data systems.

How data is stored

Computers store data by encoding it into binary values, which consist of patterns made up of the numbers 1 and 0. The smallest unit of data is called a bit.The measurement units for data continue to expand as the collection and storage of data grow. For instance, a relatively recent term called “brontobyte” represents data storage equivalent to 10 to the power of 27 bytes.

Data can be stored in various file formats, such as ISAM and VSAM used in mainframe systems. Other formats like comma separated values are also utilized for data storage, conversion and processing. These formats have found applications across different types of machines, even as more structured approaches for handling data gained popularity in corporate computing.

With the emergence of databases, database management systems and relational database technology, there has been significant specialization in organizing information.

Types of data

Different types of data have emerged due to the rapid growth of the internet and smartphones over the past decade. Data now encompasses text, audio, video information, as well as log and web activity records. A substantial portion of this data falls under the category of unstructured data.

The term “big data” has been coined to describe datasets in the petabyte range or larger. It is often defined by its three characteristics known as the 3Vs; volume, variety and velocity. As web based e commerce has expanded its reach, business models driven by big data have evolved where data itself is treated as a valuable asset.

These trends have also led to increased concerns about the social implications of data and the importance of data privacy. Data holds significance beyond its role in computer applications focused on processing information. For instance, the term “data” is often differentiated from “control bits” and similar expressions to indicate the primary content within a unit of transmission.

Advantages of Quantitative Data

• Benefits of in depth research; Conducting thorough research is highly feasible with quantitative data, as it allows for statistical analysis.
• Reduced bias; Personal bias can often lead to inaccurate results in research. However, the numerical nature of quantitative data helps minimize personal bias and ensures more accurate findings.

Disadvantages of Quantitative Data

Drawbacks to consider with quantitative data include;

• Question dependency; The type of questions asked during data collection can influence the results obtained from quantitative data. Hence, researchers must carefully consider their research objectives and question formulation while collecting such data.
• Limited descriptive information; Unlike qualitative data, which provides rich descriptions, quantitative data may not offer detailed insights. This can make it challenging for researchers to make informed decisions solely based on numerical information.

How does a computer process data into information?

To convert raw data into meaningful information, computers employ the following four functions using software and hardware;

Input;

The initial step involves feeding the computer with input. For example, you can input by typing on a keyboard.

Processing;

Using specific programs, computers process the received input into valuable information.

The program is capable of arranging, computing or manipulating the data in order to generate comprehensible information.

Result

Once the data has been processed into information, it is presented as output to the user. For instance, when using the Windows Calculator, the program showcases the information on your computer screen.

Retention

Lastly, the generated information is stored on the computer for future access. This can be done through means such as a floppy disk or other storage devices. 

The process of collecting quantitative data

There are two primary methods for collecting quantitative data, which are outlined below;

1. Surveys

Surveys have traditionally been conducted using paper based methods but have gradually transitioned to online platforms. In surveys, closed ended questions play a significant role in gathering quantitative data. These questions provide answer options for respondents to choose from. Surveys are commonly used to collect feedback from a specific audience. Depending on the time required to complete them, surveys can be categorized as follows;

Longitudinal Studies; Market researchers conduct surveys over a specific period of time, making it a form of observational research. This type of survey is often employed when the main objective is to analyze patterns in the collected data.

Cross sectional studies; Market researchers conduct surveys at a particular point in time to gain insights into a specific subject within a sample by implementing questionnaires.

To effectively administer surveys and collect quantitative data, the following principles should be considered;

 Utilize Different Question Types; Closed ended questions should be used in surveys for collecting quantitative data.

These inquiries can consist of various question types, including multiple choice questions like rating scale questions, semantic differential scale questions and others. Their purpose is to gather data that can be comprehended and analyzed effectively.

Basic Measurement Levels; The gathering of quantitative data relies on four measurement scales that serve as the foundation for constructing multiple choice survey questions.

Conclusion

Based on the available information, it seems like you’re looking for a brief summary regarding data concerning five students. Although I don’t have specific details or context about the data, I can offer a general conclusion;

When analyzing the information for these five students, it’s crucial to take into account their unique characteristics, performance levels and individual needs. By delving deeper into the data and examining aspects such as their academic progress, areas where they can improve and providing customized support, educators and institutions can make well informed decisions to enhance these students educational journey.