Table of Contents
Data and information are interconnected. Data is a collection of raw particulars and statistics. The meaningful, ordered and processed form of data is called information. Each student’s test score is an example of data while. The average score of a class or school is an example of information.
Data is a collection of raw particulars and statistics that have not yet been processed to get their accurate meaning. data is represented by alphabets (A-Z, a-z), digits (0-9) or special characters (+,-,/,*,<,>,=). It may consist of facts, characters, signs, and pictures also.
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With the advancement in technology, data is available now in the form of text, video, and audio. Mostly this type of data is in unstructured form.
Term the Big Data is also considered a type of data that is used to explore the data that is in the petabyte range or higher. Big Data is also manipulated as 5Vs: variety, volume, value, veracity, and velocity.
Due to the horrible situation around the globe due to Covid-19, web-based eCommerce has extended at a large scale, business models based on Big Data have been developed, and they consider them as the most important asset. The term big data provides huge benefits to businessmen as it reduced costs, enhanced efficiency enhances sales, and much more.
Qualitative data is data that describes the info object features without specifying quantities or sizes. There are three sub-types of qualitative data:
Nominal data or Nominal Attribute data types mean that they’re associated with names and their values are nominal values or symbols.
Examples include the names of things or people. The nominal data isn’t subject to ranking and may represent specific categories or classifications For example: during a company’s sales database, some fields can have attributes like (marital status), for which the possible values are:
And also (a profession that the possible values can be)
Thus, these values are nominal values, so these characteristics (marital status, profession) are called nominal attributes.
However, sometimes an attribute could also be nominal and therefore the values in it contain numbers, but they’re treated as nominal values. An example of this is often the (phone number) or (postal code) field. Numbers in these cases are nominal values because they can’t be added, subtracted, or mathematically compared with one another.
Boolean data or data types with a logical attribute are nominal data also, but their values are limited to 2 values or two states only. And they are often expressed numerically using the binary numeration system of numbers with two values (0,1), where the worth (zero) expresses The absence or non-fulfillment of the characteristic and the value (1) for its realization.
Ordinal data or data types with an ordinal attribute are data that will take values that have a selected arrangement between them in which order is meaningful. But without attention or maybe without the necessity to know the particular difference between the successive values during this arrangement.
For example, during a nutriment restaurant, several options are often used for the dimensions of the beverage that’s chosen with the meal. They take the subsequent value (small, medium, large), and these values make a transparent order showing the sequence of size from smallest to largest despite.
That we may find one among the variables or features that determine the customer’s opinion of a specific product, so it takes one among the subsequent values: (very bad, bad, good, very good)
All data of the nominal, logical, and ordinal type are qualitative data, that is, they describe the features of the info object without specifying its quantities or sizes.
Quantitative data or numeric data or attributes of quantitative or numeric data are sorts of data with measurable values, and that are often expressed in whole or real numbers. It also can be within the sort of Period measurement.
Interval measurement data, during which the values are divided into equal intervals, and therefore the values of those periods have a big order. These values are often positive, negative, or maybe zero. They are often compared with one another and therefore the difference between them can be calculated.
For example, the units of measurement wont to measure temperatures are measured using a Celsius scale on different days of the week. Where a selected measurement is often obtained a day, and these values are often arranged in descending or ascending order to seek out the most well-liked or coldest days.
Ratio-Scaled data are the kinds of knowledge with a numerical characteristic, during which the worth of zero may be a real value, and that they are often compared together. and They are often arranged and perform calculations on them, and statistical values are calculated for them like mean, mode, etc. Examples of Relative measurement data (product price, age, income)
For example, If we’ve two products whose prices are respectively (100) and (50), then we will say that the price of the primary product is twice the worth of the second product
Information is meaningful, ordered, and processed form of data. It is extra important than data because decisions are made by using it. Data is utilized as input and the information is the output of this processing. This information can be exercised again in some other processing.
Data and information are interconnected and closely related to each other. Furthermore, Information cannot be compiled without data. Data is an unsystematic, unorganized and unrelated entity. While information is systematized, organized, and understandable. Data is independent but the information is dependent.
The data processing life cycle is the set of steps necessary to switch data into useful information. The main purpose of this processing is to generate actionable information. Stages of the data processing life cycle are collection, preparation, input, processing, output, and storage.
Data and information are interconnected and closely related to each other. Furthermore, Information cannot be compiled without data. It will be meaningful if data is collected from the right resources. Data is the raw facts and figures while Information is a processed and meaningful form of that data.
Data and information are interconnected. The main difference between data and information is, data usually consist of raw facts or figures that have not been arranged, analyzed, and processed while information is arranged, analyzed, and processed form of raw facts or figures. To learn more about the difference between data and information, just click on the below button.
For example, marks of a student in dissimilar subjects are the data. To compute the total marks, the marks of different subjects are exploiting as data, and total marks are the information. Now, to estimate the average marks of the students, this information will be also processed once more. In this processing, the information is used as data and average marks will be the information.
Examples of Data
Examples of Information
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