In the relational data model, data is organized into tables called relations. Each relation consists of rows, known as tuples, and columns, known as attributes. The relational model provides concepts and operations that help maintain data accuracy, consistency, and efficient retrieval. Candidate key, functional dependency, referential integrity, selection, and projection are important concepts used in database design and data analysis. A Candidate Key is an attribute or a combination of attributes that can uniquely identify every tuple in a relation. A relation may have more than one candidate key, but one of them is selected as the primary key. A candidate key must satisfy two conditions: uniqueness and minimality. Uniqueness means that no two records can have the same key value, while minimality means that no unnecessary attribute is included in the key. For example, consider a data-science-oriented `Student` relation containing `Student_ID`, `Email`, `Name`, `Course`, and `Marks`. Both `Student_ID` and `Email` may uniquely identify a student. Therefore, both can be candidate keys. If `Student_ID` is selected as the primary key, `Email` remains an alternate candidate key. Candidate keys help data scientists identify individual observations without ambiguity. A Functional Dependency describes a relationship between attributes in a relation. An attribute or group of attributes on the left side determines the value of another attribute on the right side. It is represented as X → Y, meaning that if two tuples have the same value of X, they must have the same value of Y. For example, in a data-science dataset called `Customer`, suppose `Customer_ID → Customer_Name, Age, City`. This means that