Page 163 - Ai_V1.0_Class9
P. 163

For example, "Learning AI is fun." Qualitative data is further classified into two types that includes,
                 ●   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.
                 Numeric data can be further classified as:
                                  Continuous Data                                       Discrete Data
                  Continuous data can take any numeric value within a  Discrete  data  refers  to  distinct  single  values.  It
                  specified range.                                    consists of whole numbers without decimal parts that
                                                                      represent distinct categories or values.
                  Continuous data is measurable.                      Discrete data is countable.
                  This type of data can be infinitely subdivided and   Discrete data cannot be subdivided meaningfully.
                  often includes decimal points.
                  Often used to analyse using statistical techniques   It  is  used  to  analyse    using  frequency  distributions,
                  such as mean, median, standard deviation, and       bar charts, and probability distributions.
                  correlation.
                  Examples: dimensions of classroom, height, weight,   Examples: number of girls and boys in class, number
                  temperature, time, etc.                             of subjects in class 9th, count of anything.


                 Quantitative Data versus Qualitative Data

                                  Quantitative Data                                    Qualitative Data
                  Data is depicted in numerical terms.                Data is not depicted in numerical terms.
                  Can be shown in numbers and variables like ratio,   Could be about the behavioural attributes of a
                  percentage, and more.                               person, or things.

                  Examples: 100%, 1:3, 123                            Examples: loud behaviour, fair skin, soft quality, and
                                                                      more.


                 AI Domains and Type of Data

                 Various types of data are utilised across different domains to train models, make predictions, and generate
                 insights. Here are the types of data commonly used in three key domains of AI:


                 Natural Language Processing (NLP)
                 Types of data used in NLP are:
                 ●   Textual data: This includes a wide range of written text, such as articles, books, emails, social media posts,
                    web content, PDF files, etc.
                 ●   Audio data: Audio recordings of spoken language, which are transcribed into textual data.

                                                                                                Data Literacy   161
   158   159   160   161   162   163   164   165   166   167   168