Different Types of Data Explained With Simple Examples

In the simplest terms, data is information that can be collected, stored, and analysed to make decisions. Consider your daily lifestyle. Even if you don’t call it that, data is all around you. For instance:

• A student who received a test score of 90 out of 100
• The hue of your vehicle
• The outdoor temperature of your house
• Your fitness tracker’s step count

Each of these is a data point. However, they are not the same types of data, and that difference matters a lot in data science.

In data science, how data is processed, analysed, and visualised depends heavily on its type. Treating all data the same can lead to wrong insights, flawed models, and poor business decisions.

Understanding various types of data helps professionals:

• Choose the right statistical techniques
• Apply suitable machine learning algorithms
• Visualise information correctly
• Avoid costly analysis errors

• Data types are the foundation of data analysis, determining how data should be acquired, stored, analysed, and visualised.
•  When different types of data are misunderstood, analysis might become useless, such as attempting to compute the average hair colour or arranging cities by eye colour.
• Different types of data exist because information serves various functions in real-world analysis.
•  Gender, nationality, product categories, and client segments are some of the major types of data utilised to categorise information.
•  Other Types of data used to measure quantities include height, weight, revenue, temperature, and time.
•  Analysts can rate or compare values like customer satisfaction, exam marks, and performance ratings.
•  Some data formats are necessary for spotting patterns and projecting trends, such as monthly sales figures.
• Some data types are essential for identifying patterns and predicting trends, such as monthly sales figures or website traffic over time.

There are two broad categories into which all types of data will fall: Qualitative Data and Quantitative Data. It’s imperative to understand these types of classifications because each type has different ways of being assessed, viewed and interpreted. So let’s take a closer look at these types.

1. Qualitative or Categorical Data

Qualitative data, often known as categorical data, consists of qualities, characteristics, or attributes that cannot be expressed using numbers. Rather than measuring values, this type of data classifies information into distinct groups or categories. It focuses on describing what kind of data something is, not how much or how much.

Qualitative data can appear in many forms, including:

• Text, such as written responses or feedback
• Images, like photographs or icons
• Audio, such as recorded interviews or voice notes
• Symbols, including labels or identifiers
  

Common examples of qualitative data include gender, eye colour, language spoken, food preferences, opinions, and brand choices. Since this data does not involve numerical values, it cannot be averaged or used in mathematical calculations.

Businesses rely heavily on qualitative data to gain deeper insights into:

• Customer preferences and customer behaviour
• Brand perception and reputation
• User experience and satisfaction
• Market trends and consumer sentiment

The Qualitative Data Are Further Classified Into Two Parts

A. Nominal Data

Nominal data is the simplest type of qualitative data. It is used to label or name categories that are not ordered, ranked, or hierarchical. The term nominal is derived from the Latin word nomen, which means “name,” and clearly indicates its purpose: designating categories.

In nominal data, there is no notion of one value being more or less than another.  For example, hair colour and nationality cannot be ordered or compared numerically.

Examples of nominal data include:

• Hair colour (Black, Brown, Blonde)
• Gender (Male, Female, Others)
• Marital status (Single, Married)
• Nationality (Indian, American, British)
• Eye color

Nominal data is widely used in:

• Surveys and questionnaires
• Demographic studies
• Customer segmentation and profiling

B. Ordinal Data

Ordinal data is similar to qualitative types of data, but with one significant difference: it has a meaningful order or ranking. While ordinal data allows us to compare values as higher or lower, the precise intervals between categories cannot be determined.

For example, “Very Happy” is better than “Happy,” but there is no way to quantify how much better it is.

Ordinal data is commonly found in:

• Feedback and satisfaction surveys
• Performance evaluations
• Rankings and ratings

Examples of ordinal data include:

• Customer satisfaction levels (Very Happy, Happy, Neutral, Unhappy)
• Exam grades (A, B, C, D)
• Competition rankings (1st, 2nd, 3rd)
• Economic status (High, Medium, Low)
• Education levels

Here’s the major difference between Nominal and Ordinal data: 

2. Quantitative Data

Quantitative data is information in numerical form that is used to measure quantities. Unlike qualitative data, it answers questions like how much, how many, and how frequently. This makes it indispensable for statistical analysis, forecasting, and machine learning models.

Examples of quantitative types of data are:

• Height and Weight
• Temperature
• Test scores
• Time Duration

Because quantitative data is numerical, it may be analysed using mathematical formulas and represented using charts such as bar charts, line graphs, histograms, and scatter plots.

The Quantitative Data Are Further Classified Into Two Parts

A. Discrete Data

Discrete data consists of countable values that can be represented as whole numbers. These values cannot be broken down into smaller quantities or stated as decimals.

Examples of discrete data are:

• Number of students in a class.
• Number of employees in a company.
• Total number of days in a week
• Total goals scored in a match.

Discrete data is frequently represented as bar graphs or frequency tables and is widely used in operational and performance analyses.

B. Continuous Data

Continuous data are quantifiable quantities that can take any value within a specified range, including decimals and fractions. This form of data is measured instead of being counted.

Examples of continuous data are:

• The height of an individual
• Speed of a vehicle
• Time required to finish a task
• Temperature

Histograms and line graphs are the most effective ways to visualise continuous data, particularly for analysing trends or fluctuations across time.

*datasciencedojo.com

The table below represents the difference between Discrete and Continuous data:

If you want to be a data expert, here are a few easy tips to follow: 

1. Create a Solid Foundation in Data Concepts

Understand the basic concepts of data theory. These include types of Data: Nominal, Ordinal, Continuous, and Discrete; Statistics; Data Visualisation (Graphs and Charts). By knowing these basic principles, you will be able to better assess how to interpret a given piece of data, and you will have more options when determining the best way to analyse it.

2. Learn Essential Tools and Programming Languages

Next, you should learn to work with the most commonly used programming languages and tools in data analysis. They are: Python, R, SQL, and Excel. Visual Analytics Software Tools include Tableau, Power BI, and Google Data Studio. These languages and tools will give you hands-on experience analysing and visualising data, and provide insights to transform raw numeric data into valuable insights.

3. Work on Real-World Projects

Once you understand the above concepts and have learned to use them, begin creating your own real-world projects using methods for analysing data: Sales Reports, Customer Reviews, and Web Analytics. Working on these projects will allow you to gain greater experience and hone your knowledge.

4. Take Online Courses and Certifications

The best way to hone your knowledge and make your resume stand out is by taking online courses and certifications. For example, the PG Certificate Programme in Applied Data Science AI-IIT Roorkee through Jaro Education can help you analyse various types of real-world datasets and produce actionable insights for application in the economy, business, and society. 

5. Participate in Data Challenges and Collaborate

Participate in Kaggle competitions, hackathons, and community projects to test your abilities in real-world settings through collaboration. Working with other data professionals can help you gain knowledge and expertise about real-world challenges while getting constructive feedback on your work from someone else.

Understanding different forms of data is a critical ability in data science and analytics. Whether it be nominal data, ordinal data, discrete data, or continuous data, each type has a distinct role in analysis and decision-making.

If you want to pursue a career in data science, learning these principles is the first step. Good foundations lead to good careers, which all begin with an awareness of facts.