Data is the organized collection of numerical and factual information gleaned via digital exchanges.

Different kinds of statistical data can help us make better forecasts about the future and enhance the quality of services we already offer.

Millions of businesses have benefited from the data stream's ability to assist them to make growth-oriented decisions with more consistency and accuracy. Data is a large collection of information that has been divided into several categories, or "data types," to acquire data of varying qualities and features.

Because of the gravity of the situation, this data must be safely and accurately stored and processed. Datasets can be categorized into different data, each of which requires a different preprocessing approach or statistical analysis to yield the best results.

Nominal

There is no obvious hierarchy among these values. So, let's look at some examples to see what I mean. Because we can't directly compare one smartphone color to another, we can classify smartphone color as a nominal data category.

Claiming that "Red" is superior to "Blue" is impossible. Also, there's no way to tell if a person is male, female, or neither of those. Nominal data can also describe the price range of a mobile phone or the quality of a smartphone.

In statistics, nominal data types cannot be reduced to a numeric value. When conducting qualitative research, it is helpful to collect nominal data because it gives respondents more leeway in expressing their opinions.

Ordinal

These values preserve their class and can be ranked straightforwardly. Small, medium and large are the most intuitive categories to use when categorizing clothing brands by size.

A+ is unquestionably superior to B+, hence the grading scale used to determine test taker scores can be thought of as an ordinal data type. We can use these classifications to determine whether the encoding method is appropriate for a given set of data.

Since the underlying mathematics of machine learning models makes it impossible for them to work with qualitative values in their raw form, such data must be encoded before being fed into the models.

Nominal data types, where comparisons cannot be made between categories, lend themselves to one-hot encoding, which is analogous to binary coding because there are fewer bits to encode, while ordinal data types lend themselves to label encoding, a form of integer encoding.

Discrete

Integers, often called whole numbers, are the types of numbers that belong here. Phone features like speaker count, camera resolution, processor core count, and the number of compatible SIM cards are all examples of discrete data.

It is impossible to obtain an accurate discrete value from discrete data in statistics; all that can be done is to count the number of instances of the data type.

You're free to use decimal notation for the value, but it must be an integer or a whole number. Bar charts, pie charts, and tally charts are common examples of the kinds of charts used to visually represent discrete data.

Continuous

In this case, the fractional numbers are treated as continuous values. The processor's operating frequency, the phone's Android version, the wifi frequency, the core temperature, and so on are all examples of such variables.

Researchers often use continuous data since it can be broken down into smaller bits and can have any value, as opposed to discrete data types that have a whole and fixed value.

Changeable quantities like temperature and body mass index (BMI) can both be accounted for in the continuous value.

The highs and lows of a line over time make it easy to see how values change over time when shown as a graph representing continuous statistical data.

Significance of Both Quantitative and Qualitative Information

Qualitative data in research centers on the features of the recovered material and aids in the comprehension of patron conduct.

These statistics help conduct market research with accurate numbers and for developing a service's worth through the incorporation of relevant data.

Statistics' qualitative data kinds, when used wisely, can have a major impact on client happiness.

However, the Quantitative data categories of data deal with measurable quantities, providing answers to questions like "how much," "how many," and "how often."

In statistics, data designated as "quantitative" are taken very seriously. Consequently, businesses can benefit from their assistance in analyzing these numbers for trends and errors.

Final Words

we have now reached the concluding parts of this article. We thoroughly discussed the different data types. If you believe you are great with numbers and data, data science is the ideal career option for you.

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