Introduction to Data Science

Data Science Introduction

• Different forms of datasets exist: Record data, Graph and networks, Ordered data.
• Record data includes relational tables, data matrix, transaction data, and document data.
• Relational tables are highly structured, using columns and rows for efficient data storage.
• Data matrix encompasses numerical matrices and crosstabs, presenting data in table form.
• Transaction data captures detailed individual transactions, often used for analysis and understanding customer behaviour.
• Document data utilizes term-frequency vectors (matrix) to analyze textual content, identifying key terms and relationships.
• Entity refers to a specific object or thing.
• Attribute is a measurable or observable characteristic of an entity.
• Data is the measured value of an attribute.
• Data Categorization classifies data into quantitative and qualitative, each having distinct properties.
• NOIR topology categorizes data based on its properties - Nominal, Ordinal, Interval, Ratio.
• Nominal data is categorized without any inherent order, represented by labels like gender or blood type.
• Ordinal data has an order, allowing comparison of categories, for example, ranking of students.
• Interval data has order and equal intervals between measurements, allowing for addition and subtraction.
• Ratio data is similar to interval data but has a true zero point, allowing for multiplication and division.
• Binary Scale is a special case of nominal scale, with only two mutually exclusive categories without inherent ordering.
• Symmetric binary variables can be reversed, like "on/off" or "true/false".
• Asymmetric binary variables are not reversible, like "positive/negative" or "male/female".
• Nominal scale utilizes labels for categorization without inherent order, using letters, numbers, or strings.
• Binary scale is a special case of nominal scale, with only two categories.
• Ternary scale has three categories, while others have more than two.
• Nominal data, even when represented numerically, lacks mathematical interpretation.
• Labels in nominal data can be combined to create new nominal variables.
• The number of categories in nominal data should be countably finite.