Data science involves experimenting with structured or raw data. Data can be the fuel that drives a business on the right track or provide insights that can help you strategize your campaigns, launch new products or test out different ideas.
Data is the common driving force behind all these things. The digital age is here, and we are producing a lot of data. Flipkart, for example, produces over 2TB of data per day.
This Data is so important in our lives that it is essential to store and process it correctly. Datasets can be classified by their types of data to help determine the preprocessing strategy that would work best for each set of data or the type of statistical analysis that should be used to achieve the best results. Let’s take a look at some commonly used different types of data.
What are the types of data?
Qualitative Data Type
Categorical or Qualitative Data describes an object using a finite number of distinct classes. This data type cannot be easily measured or counted using numbers and is therefore not possible to divide into categories. This data type is useful for determining the gender of an individual (male, female or other).
This information is often extracted from audio, images, or text media. A smartphone brand might also provide information such as the current rating, color, category, and so forth. Qualitative data can include all of this information. This category includes two subcategories:
These are values that lack a natural order. Let’s look at some examples to illustrate this. A smartphone’s color can be considered a nominal data type since we cannot compare colors.
It is impossible to say that ‘Red is more than ‘Blue. Another area where it is difficult to distinguish between male and female is the gender of an individual. Nominal data types can also be used for mobile phone categories, whether they are midrange, budget, or premium smartphones.
These values are natural in their order while still maintaining their value class. Considering the size of clothing brands, we can sort them by their name tags in the order of small to medium to large. You can also consider the grading system used to mark candidates during a test as an ordinal data type, where A+ is certainly better than B grades.
These categories allow us to determine which encoding strategy is best suited for which type of data. Data encoding of Qualitative data is crucial because machine learning models cannot handle these values directly. They must be converted to numerical types since the models are mathematical.
One-hot encoding is possible for nominal data types where there is no comparison between the categories. This is similar to binary code since there are fewer numbers. Label encoding is an alternative to integer encoding.
Quantitative Data Type
This datatype attempts to quantify things by using numerical values that make them countable in nature. The price of a smartphone or discount, ratings and reviews on a product as well as the frequency of the processor used in a smartphone’s operation, and the ram are all part of the Quantitative data type.
It is important to remember that a feature can have infinitely many values. A smartphone’s price can range from x to any value, and can also be broken down using fractional values. These two subcategories are the best to describe them.
This category includes integers and whole numbers. Examples of discrete data types include the number of speakers, cameras, processor cores, and a number of supported sims.
Fractional numbers are not considered continuous values. These could be the operating frequency of processors, android version, wifi frequency, core temperature, and so forth.
Can ordinal and discrete types overlap?
This is how you can add numbering to ordinal classes. Then it should be called either discrete type or ordinal. It is still ordinal, however. This is because even though the numbering is accurate, it doesn’t show the distances between classes.
Consider, for example, the grading system for a test. You can choose to have the respective grades A, B C, D, or E. If you start counting them, it would be 1,2,3,4,5. The numerical differences show that the distance between E and D grades is equal. This is not accurate because C grade is still acceptable compared to E, but the mid difference declares them equal.
The same technique can be applied to surveys where the user experience is measured on a scale from very poor to very excellent. It is difficult to quantify the differences between different classes because they aren’t easily quantifiable.
We’ve covered all major data classifications. This is crucial because it allows us to prioritize the tests that will be done on different categories. It makes sense to plot a frequency plot or histogram for quantitative data, and a pie chart or bar plot for qualitative data.
Only quantitative data can be used for regression analysis. This is where one dependent variable is analyzed and two or more independent variables are analyzed. ANOVA (Analysis by variance) is only applicable to qualitative variables. However, you can use two-way ANOVA tests which use one measurement variable and two nominal variables.
This allows you to apply the Chi-square test to qualitative data in order to discover relationships between categorical variables.
We discussed in this article how data can flip the tables and how different data types are organized according to their needs. We also examined how ordinal data types may overlap with discrete data types.
We also discussed which type of plot is best for what type of data, and the various types of tests that can be used on that type of data.