Types Of Data That Data Scientists Use
Data can be broadly classified into two types - Qualitative and Quantitative.
It is a type of data that is used to gain an understanding of the underlying reasons and motivations behind people's behaviour. It is often used in research studies to explore the attitudes, beliefs, values, and opinions of individuals. Qualitative data can be collected through interviews, focus groups, surveys, observations and other methods. By collecting qualitative data, researchers can gain insight into how people think and feel about certain topics or issues.
Nominal data is a type of data that is used to classify and categorize information. It is typically used to identify and differentiate between different objects, people, places, or events. Nominal data can be used to classify items such as gender, age group, or occupation. It can also be used to identify the characteristics of an item such as colour, size, or shape. The use of nominal data helps researchers gain insight into the relationships between different variables and can provide valuable information about trends in a given population.
Ordinal data is an important type of data used in data science. It is a form of categorical data that includes information about the order or ranking of objects. This type of data can be used to measure and compare different attributes, such as customer satisfaction or product quality. It is also helpful for understanding relationships between variables, such as the correlation between income and education level.
Quantitative data is one of the most important forms of data used in research and decision-making. It is a type of information that can be measured and quantified, such as numbers, percentages, or other numerical values. This type of data can provide valuable insights into trends and patterns that would not be possible to discern from qualitative data alone.
There are two subcategories of Quantitative Data: Discrete Data and Continuous Data
Discrete Data is a type of numerical data that can only take on certain values. These values are usually integers or whole numbers, such as 1, 2, 3, 4 and so on. Discrete data is useful for many applications because it allows us to easily measure and compare different values. For example, we can use discrete data to measure the number of people in a room or the number of products sold in a store. Additionally, discrete data can be used to compare different groups or categories of objects or people to identify patterns and trends.
Continuous Data is shown using fractional numbers because it includes fractions, decimals, and other non-integer values. Continuous data is often used in scientific research and statistical analysis to measure changes over time and to compare different sets of data. It is also used in machine learning algorithms for predictive modelling. Most Machine Learning Online Courses teach this in detail. With continuous data, data scientists can gain insights into trends and patterns that would otherwise be impossible to detect. Also, it is essential for anyone working with large datasets or who needs accurate results from their analysis.