Data is the heart and soul of data science. So, understanding different data types is important to analyse and interpret the data accurately. There are four types of data: Nominal, Interval, Ordinal, and Ratio. Out of all, nominal data is considered one of the most fundamental data types. So, what is nominal data? Nominal data is a categorical data type comprising discrete values assigned to categories. It is important to understand what is nominal data as it is commonly used in various fields, such as marketing, social sciences, etc. Moreover, understanding the characteristics of nominal data is essential for accurate data analysis and interpretation in data science.
Now that you know what is nominal data, here are its characteristics -
Nominal data is typically collected through surveys, questionnaires, or other data collection instruments. All these formats seek to collect relevant information from respondents. For example, a survey might ask respondents to indicate their gender. So, it will have options like "male", "female", and "trans."
Another example could be a survey asking people about their level of education. It will have options like "high school", "college", "graduate school", and so on.
The categories or labels must be mutually exclusive and exhaustive when collecting nominal data. Each observation can only belong to one category and cover all possible options. For example, there is a survey about political affiliation. So, the survey should include options for all relevant political parties or ideologies.
Nominal data is used in various fields. From marketing to healthcare, nominal data plays a very crucial role. It helps to identify patterns, trends, and relationships among different categories or labels. For example, nominal data might be used in marketing research to analyse consumer behaviour and preferences. By collecting data on the products people buy, researchers can analyse the most popular products.
Here is how nominal data is used -
Nominal data is commonly used to study demographic characteristics. These characteristics include age, gender, race, and education level. Researchers can identify patterns and trends among different people by collecting this data. For example, a survey might collect nominal data on participants' age and education levels. It can later analyse whether there are any correlations between age and education.
Nominal data is frequently used in marketing research. It is usually used to analyse consumer behaviour and preferences. For example, a survey might collect nominal data on the brand preferences of different age groups. This data can be used to analyse which brands are most popular among each group.
Nominal data can help identify which categories are most prevalent in the market. It can also be used to see if there is any correlation between different categories or groups. For example, a researcher might collect data on the political affiliation of different voters. It can be used to analyse whether there are any patterns or trends in how different groups vote.
Nominal data is used to train algorithms to recognise different categories or groups. Machine learning algorithms can learn to make predictions based on new data by providing a large dataset of labelled data. For example, a machine learning algorithm might be trained on a labelled image dataset. Here, each image is labelled with the category of the object it contains (e.g. "car", "person", "animal").
Here is the step-by-step process for analysing nominal data -
1. Collecting and Organising Nominal Data
The first step is to collect the data. This can be done using surveys, questionnaires, or other data collection methods. Once the data is collected, organising it into categories or labels is important. This ensures that each category or label is mutually exclusive and exhaustive.
2. Determining Frequency Distributions
Once the data is collected and organised, the next step is determining the frequency distribution for each category. This involves -
3. Analysing Mode and Median
Once the frequency distributions are determined, the mode and median can be analysed. The mode is the category or label that appears most frequently in the dataset. On the other hand, the median is the middle category or label in the dataset.
For example, consider there is a survey on favourite colours. The frequency distribution shows that 50 people selected blue, 30 selected red, and 20 selected green. Here, blue is the mode, and the median would depend on how the categories are ordered.
4. Comparing Frequency Distributions
The next step is to compare frequency distributions across different groups or categories. This can be done using cross-tabulation or contingency tables. These representations show frequency distributions differ across different categories.
5. Examining Relationships Between Variables
Nominal data can be used to examine relationships between variables. This can be done using statistical tests like chi-squared tests or correlation coefficients. For example, a survey might collect nominal data on the favourite colour of different genders. It can be used to analyse if there is a significant correlation between gender and colour preference.
Consider you are working for a music streaming platform. You want to understand the music preferences of your users. So, you decided to collect data from a random sample of 1000 users and ask them to select their favourite music genre from a list of options. The options are rock, pop, country, hip-hop, and classical.
After collecting the data, you will label and categorise the answers based on the music genres. Further, you will also count the number of users who selected each genre. So, you find that -
Now, you will visualise the data using a pie chart. It will reflect the proportion of users who selected each genre. The pie chart will read that rock is the most popular genre, followed by hip-hop and pop, while classical is the least popular.
The next step is to determine any significant differences in music preferences among different age groups in the data. For that, you can opt for the chi-square test. You find a significant difference in music preferences between users under 30 and over 30. Younger users are more likely to select hip-hop. However, older users are more likely to select classical.
Finally, you can draw insights from the analysis and use them to make decisions about the music on your platform. For example, you may decide to focus more on promoting rock and hip-hop to younger users and classical to older users.
Now that you know what is nominal data and that it is one of the foundational concepts in data science. It will not be wrong to say that analysing and interpreting data without a solid understanding would be difficult. Moreover, mastering nominal data makes it really easy to do a range of datasets and draw meaningful insights from them. That is why top companies prefer to hire professionals with Data Science training. After all, a trained professional ensures the best outcomes. With a 100% job guarantee, 6 months of Certified Project Experience, and live training with a dedicated mentor, enrol in this Data Science Certification Course and give your career a big break!
1. Can nominal data be converted to numerical data?
Nominal data cannot be converted to numerical data. It is because the categories do not have any numerical value associated with them.
2. Why is it important to properly label and categorise nominal data?
It ensures that data is accurately represented and can be used for meaningful analysis.
3. Can nominal data be used in regression analysis?
Nominal data cannot be used in regression analysis. It lacks numerical values and cannot be placed on a scale.
4. What are some common mistakes when working with nominal data?
Common mistakes when working with nominal data include:
5. How can nominal data be visualised?
Nominal data can be visualised using bar charts, pie charts, and stacked bar charts. These visualisations can help to illustrate the distribution of categories within the data.
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