Data Handling is a process of organizing and presenting raw data in a form that can be used for fetching and inferring useful information. This data may comprise numbers, names, facts, or any other kind of description of the given entities. Data is commonly presented through graphs, tables, charts, etc.
Data is generally classified into the following two types:
Qualitative data denotes the non-countable features and traits of entities that cannot be measured or expressed in numbers. For example, colors, names, etc. On the other hand, quantitative data indicates measurable characteristics like length, width, height, etc. Further, quantitative data can be continuous or discrete. While the continuous data can take any value in the given range, the discrete data can only possess certain fixed values, such as the whole numbers.
Data is classified and tabulated to systematize comparison and utility of the collected information. Once the required data is gathered, it is then classified according to its similarities and dissimilarities. Classification implies the arrangement of data into homogeneous classes or groups based on distinct attributes and variables. The major types of classifications are as follows:
After classification, data is displayed and summarized in tabular format. The logical arrangement of data in rows and columns is called data tabulation.
When there is a need to examine a huge bulk of data, it is often grouped in specified ranges or class intervals. In other words, the ungrouped data refers to individual data points, whereas the grouped data is presented in a certain range of classes. The latter is preferably shown through frequency distribution tables, where frequency implies the number of observations for each specific group. The following tables illustrate an example of the frequency distribution of ungrouped and grouped data, respectively:
|Marks Obtained||No. of Students (Frequency)|
|Marks Range||No. of Students (Frequency)|
The above tables exhibit the marks obtained by 15 students. The ungrouped data is converted into grouped data by categorizing marks in fixed intervals of size 10. It is important to note that the upper limit of a class is not included in that interval. For instance, the students who scored 20 are counted in the interval 20-30 and not in the interval 10-20.
The graphical representation is a diagrammatic organization of the given data. Some of its most popular forms are:
The representations mentioned above prove effective for any type of numerical data, which is why these are widely used in statistics.
A bar graph is the simplest means to represent any given data. In this, equally spaced bars vary in length/height as per the given variable’s value. Bar graphs can also be used to denote the frequencies of specific data.
The following diagram shows the number of cars that pass through a street X on three different days at particular time intervals:
For instance, 250 cars passed through the street X in the time interval 10-11 am on Saturday.
When a given data is represented in circle graphs or pie charts, each section of the particular chart shows a proportionate quantity of the whole. To draw a pie chart, you need to add data values to arrive at the whole number, which can be used to divide each value into corresponding angles of the circle. For example, in a class of 100, a teacher divided the students based on their favorite colors as follows:
|Colors||No. Of Students|
The pie chart for the above data can be drawn as follows:
The above diagram represents one of the simple pie charts with reasonable data, where each percentage value (in decimals) is multiplied by 360° to obtain the angle of the corresponding sectors.
Data handling is essentially based on the fundamental concepts of statistics. It is considered highly useful in different fields of mathematics, science, and engineering. Among various graphical representations, bar graphs and pie charts are the easiest and most popularly used modes of data handling. These prove quite efficient in analyzing prices, stocks, growth rates, and similar forms of data. Hence, it is crucial to understand the above-mentioned topics to make a mark in any professional domain.