Data Basic Test Let's test how much you have learned today! 1. What is the difference between nominal and ordinal data types, and can you provide examples of each? Answer: Nominal and ordinal data types are both categories of categorical data. The key difference is that nominal data is used for labeling variables without any inherent order or ranking, while ordinal data represents variables with a specific order or ranking, but the difference between each category is not quantitatively defined. Examples of nominal data include gender (male, female) and color (red, blue). Examples of ordinal data include satisfaction levels (e.g., very satisfied, satisfied, dissatisfied) and education levels (e.g., high school, bachelor's, master's). 2. What distinguishes discrete data from continuous data, and could you provide examples of each type? Answer: Discrete data and continuous data are both subtypes of numeric data. Discrete data represents items that can be counted and take on distinct, separate values. The list of possible values may be fixed or infinite but countable. Examples of discrete data include the number of students in a classroom, the number of vehicles in a parking lot, and the number of items in a shopping cart. Continuous data, on the other hand, represents measurements with values that are not countable and can only be described using intervals on the real number line. Examples of continuous data include height (which can have infinitely many values within a range), temperature (measured with decimal values), and velocity. 3. What are the key differences between structured and unstructured data, and can you provide examples of each? Answer: Structured and unstructured data are two different forms of data categorization. Structured data is highly organized and typically stored in a tabular format. Each row represents a single entity or case, and each column contains a single attribute or variable. Examples of structured data include databases containing information like names, dates, stock information, or geo-locations. Unstructured data, on the other hand, lacks a specific structure or organization and is often in the form of text, documents, logs, images, videos, audio, and more. Examples of unstructured data include text files, documents, sensor data, and social media content. The key difference lies in the format and organization of the data, with structured data being well-organized and unstructured data lacking a predefined structure.