Maths and me @ Data Handling
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.
Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyse, 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.
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. Quantitative 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.
