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Data can be broadly classified under Textual data and Numeric Data as explained.

                                                                    Data





                                                         Textual            Numeric




                                                                 Continuous           Discrete





                                            Nominal Data         Ordinal Data



                 Textual Data (Qualitative Data)

                 Textual data is the information that is written or expressed using words and language. It includes things like
                 articles, books, emails, messages, and any other written content. Instead of numbers, it's made up of letters,
                 words, and sentences that convey meaning and information. Qualitative data is also called categorical data.
                 For example, "Learning AI is fun."

                 Qualitative data is further classified into two types that includes,
                    • Nominal data
                    • Ordinal data



                 Nominal Data
                 It consists of categories or names that cannot be ordered or ranked. Nominal data is often used to categorize
                 observations into groups, and the groups are not comparable.  Examples of nominal data include gender (Male
                 or Female), and blood type (A, B, AB, O).


                 Ordinal Data
                 It consists of categories that can be ordered or ranked.  Ordinal data is often used to measure  opinions, where
                 there is a natural order to the responses. Examples of ordinal data include education level (Elementary, Middle,
                 High School, College), job position (Manager, Supervisor, Employee), etc.


                 Numeric Data (Quantitative Data)

                 Numeric data means information that's in numbers, not words or descriptions. It's often called quantitative data
                 because it's collected as numbers and can be used for math and stats. For instance, if you know the total number
                 of workers and how many are men, you can figure out how many are women by subtracting. This ability to do
                 calculations with numeric data makes it great for doing statistics and analysing data.
                 For example, marks, temperature, height, weight, etc.







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