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We live in a digital world that creates data faster than ever. And the information you can glean from that data is valuable.
However, it’s important to understand what to collect and how to manage and classify that data. Your company should also be able to interpret the data and turn it into insights you can use to improve your operations, make more sales, and grow your business.
Keep reading to learn about the different types of data. You can also click on the following links to read specific sections:
Why are data types important?
Data types are important because they are attributes of data that tells a computer system how to interpret their value. Understanding the different data types allows users to pick the one that matches their needs and goals.
When dealing with data sets, data scientists use data types to determine the statistical analysis they can apply to the data for the best results. In addition, understanding data types is critical for proper exploratory data analysis (EDA), which is one of the essential parts of a machine learning project. This is because data types are also a way of classification that specifies what type of mathematical operations can be applied to the variable without causing an error.
In machine learning, knowing the appropriate data types of independent and dependent variables provides the basis for selecting the right data analysis method. Data types that are incorrectly identified can lead to incorrect modeling, which can produce wrong or unhelpful information.
Data collection is an essential part of the research process and it’s important to start your project with experienced professionals. Utilize Upwork to connect with independent researchers and Big Data developers today.
Qualitative vs. quantitative data
Before discussing the differences between qualitative and quantitative data, let’s define variables. A variable is a characteristic that can be measured and can assume different values. Examples of variables include height, age, income, and nationality.
There are two main types of variables: categorical and numeric. Numerical data will always be quantitative, while categorical data will always be qualitative. You can identify the type of data before collection based on whether the variable is numeric or categorical.
Quantitative and qualitative data provide different outcomes but are often used together to get the complete picture. Here are the differences between these two types of data:
Understanding data classes and types
At the highest level, there are two kinds of data: quantitative and qualitative. These two types of data break down further into four classifications. The two subcategories of qualitative data are nominal data and ordinal data. The two classifications of quantitative data are interval data and ratio data.
These types of classification are important to machine learning, artificial intelligence, and market research because they help users choose the correct data for the analysis method. For instance, if analysts are looking for statistical results like mean and standard deviation, they’ll use quantitative values because they have numeric meaning.
Qualitative data
As mentioned, qualitative data is descriptive and can’t be counted or measured using numbers. This is also why it’s called categorical data—because the information can be sorted by category, not by number. In data science and statistics, qualitative data deals with characteristics and descriptors that can be observed subjectively.
When you classify or judge something based on smell, taste, and texture, for example, you create categorical data. Examples of qualitative data include language, nationality, and the names of countries.
Nominal data
Nominal data refers to variables that name or label a category. It’s a type of data that is observed but not measured. Nominal data has no numerical value; instead, it names a variable without applying any particular order.
Examples of nominal data include the weather, music genres, types of cuisine, and color. And since you can’t organize nominal data, you can’t sort or put it in order, either. For instance, you’ll be hard-pressed to say that the color “red” is greater than “blue.” However, you can use nominal data to count how many people like red and how many people prefer blue.
This is how scientists use nominal data—to calculate frequencies, proportions, and percentages. To get results, nominal data is transformed into a more representative numerical format that machine learning codes can easily understand.
Ordinal data
Ordinal data is a kind of statistical data with a set order or scale. This means ordinal data can be classified into different categories with a natural ranked order. However, the distances between the values are uneven or unknown.
For instance, clothing sizes are an example of ordinal data, and you can quickly sort them in the order of small < medium < large. But there is no defined way to give meaning or note the differences between small and medium or small and large.
Data science uses the categories where ordinal data is sorted and ordered to decide which encoding strategy can be applied to the data. Encoding categorical data is important because machine learning models can’t handle the values directly. Like nominal data, users need to convert ordinal data to numerical types before it can be used for machine learning models.
Additional examples of ordinal data include a person’s education level, the letter grading system, and customer satisfaction survey scales of 1 to 10. The example of a survey scale of 1 to 10 shows that ordinal data can have numerical values. However, the difference is that you can’t do any numerical activities with the values because they only show sequences.
Quantitative data
Quantitative data refers to variables with quantifiable and numerical values. Also known as numerical data, quantitative data deals with numbers and information that can be measured objectively. Analysts use this data for mathematical calculations and statistical analysis.
In turn, companies use results from data analysis to make real-life business decisions. Quantitative data can also be verified and evaluated. It also answers the questions “how many,” “how often,” and “how much.” Examples of quantitative data include temperature, prices, and dimensions like height, width, and length.
Furthermore, numeric variables can be continuous or discrete. Discrete data only accepts integers. The values can’t be subdivided into smaller parts. For instance, the number of students in a program is discrete data because you can only count whole individuals. Obviously, you can’t have certain values like 1.5 or 2.5 kids.
Discrete data also has a limited number of possible values, such as the days of the month or hours of the day. Lastly, this type of numerical data can have an infinite but countable number of values. It means there’s no fixed upper limit to the count, which makes the world’s population an example of discrete data.
On the other hand, continuous data represents the information that can be divided into finer levels and still retain its meaning. This type of data can have almost any numeric value. The continuous variables can take any value between two numbers.
Take the measurement for height, for example: You can round the numbers to the nearest whole number. But between 5 feet and 7 feet, there can be hundreds of possible values, such as 5.01234 and 6.9876.
It’s important to know whether you have discrete or continuous data because it impacts the techniques and models for analysis. If you’re unsure if the data is continuous or discrete, remember this: If the measurement can be divided into parts and the number still makes sense, the data is continuous.
Below are other types of quantitative data that may fall under discrete or continuous.
Interval data
Interval data refers to information measured along a scale with equal distances. The distances or spaces in between the adjacent values are called intervals. So, the interval scale represents information about the order and it gives meaning to the difference between two values.
For example, Celsius and Fahrenheit are examples of interval scales. Each value on these scales differs from the adjacent values by intervals of exactly 1 degree. For example, the difference between 20 and 21 degrees is identical to the difference between 225 and 226 degrees.
In addition, zero can be an arbitrary value on an interval scale, which means zero is not the lowest value. Using the example of Celsius and Fahrenheit measurement, 0 degrees isn’t the lowest possible temperature.
Ratio data
Ratio data is quantitative data that has an equal and definitive ratio between each value. Unlike interval data, ratio data has an absolute zero. It means ratio variables can’t have negative values, and zero means none of that variable is present.
For instance, the measurement of height is considered ratio data, and it’s not applicable to have a negative number for height. With ratio data, you also get a meaningful interpretation between the ratio of two values. For example, age is a ratio variable, and a 40-year-old person is twice the age of someone who’s 20.
Build a successful data-driven decision-making team
Data collection and data analysis can help you make better-informed decisions for your business, and understanding the data you have can help improve the efficiency of your operations. It can also present you with ways to provide an excellent customer experience.
Make sure you have the right team of people who know what they’re doing. Upwork is the largest network of independent professionals. Connect with talented data analysts and data scientists through our platform and get your project started today.
