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What are tuple integrity constraints?
What are tuple integrity constraints?
Constraints that are verified on the tuple level, instead of single fields.
Explain referential integrity constraints.
Explain referential integrity constraints.
Requries that the value of an attribute of a relation exists as a value of the primary key of another relation.
What is the purpose of referential integrity constraints?
What is the purpose of referential integrity constraints?
Referential integrity constraints are bidirectional.
Referential integrity constraints are bidirectional.
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A primary key must be __________ and __________.
A primary key must be __________ and __________.
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What is the purpose of the CONCEPTUAL DESIGN in database design?
What is the purpose of the CONCEPTUAL DESIGN in database design?
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What are some constructs that compose the ENTITY-RELATIONSHIP MODEL (ER MODEL)?
What are some constructs that compose the ENTITY-RELATIONSHIP MODEL (ER MODEL)?
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In a relational database, a primary key uniquely identifies each record in a table.
In a relational database, a primary key uniquely identifies each record in a table.
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ATTRIBUTES are elementary properties which describe ______ and relationships.
ATTRIBUTES are elementary properties which describe ______ and relationships.
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Match the following database design stages with their descriptions:
Match the following database design stages with their descriptions:
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What is the difference between data and information?
What is the difference between data and information?
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What are the five steps of data science?
What are the five steps of data science?
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What is Big Data?
What is Big Data?
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In the context of data science, Privacy is a major risk associated with the handling of data.
In the context of data science, Privacy is a major risk associated with the handling of data.
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The two foundations of the relational model are the _______ key and the foreign keys.
The two foundations of the relational model are the _______ key and the foreign keys.
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What does it mean to set referential integrity constraints between Exams.course and Courses.code_course?
What does it mean to set referential integrity constraints between Exams.course and Courses.code_course?
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How can the update operation violate referential integrity constraints?
How can the update operation violate referential integrity constraints?
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Adding indexes to a database will make SELECT operations faster but INSERT, UPDATE, and DELETE operations slower.
Adding indexes to a database will make SELECT operations faster but INSERT, UPDATE, and DELETE operations slower.
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In a database, if a field appears frequently in WHERE clauses of SELECT statements, it is recommended to define an ________ on that field.
In a database, if a field appears frequently in WHERE clauses of SELECT statements, it is recommended to define an ________ on that field.
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Match the following cardinality values with their meanings:
Match the following cardinality values with their meanings:
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What is a recommender system?
What is a recommender system?
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Which companies were among the very first to use recommender systems?
Which companies were among the very first to use recommender systems?
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Collaborative filtering requires knowledge of the content characteristics of items being recommended.
Collaborative filtering requires knowledge of the content characteristics of items being recommended.
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In content-based filtering, recommendations are based on the characteristics of the ________.
In content-based filtering, recommendations are based on the characteristics of the ________.
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Match the following rating methods with their descriptions:
Match the following rating methods with their descriptions:
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What is SQL primarily used for?
What is SQL primarily used for?
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What are the 3 main parts of a SELECT statement in SQL used to retrieve rows?
What are the 3 main parts of a SELECT statement in SQL used to retrieve rows?
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In SQL, the 'AND' operator requires both conditions to be true.
In SQL, the 'AND' operator requires both conditions to be true.
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In SQL, to search for specific patterns in a field, the operator 'LIKE' is used with the wildcard symbol ____.
In SQL, to search for specific patterns in a field, the operator 'LIKE' is used with the wildcard symbol ____.
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Match the following mathematical operations with their symbols:
Match the following mathematical operations with their symbols:
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Study Notes
Introduction to Data Science
- Data items: elementary descriptions of things, events, activities, and transactions recorded, classified, and stored but not organized to convey specific meaning
- Information: data that has been organized to have meaning and value to the recipient
- Big Data: a huge amount of data, used to refer to the activity of recognizing patterns and producing knowledge from the data
- Risks of Big Data: privacy concerns, as data can be used to infer information not directly provided
- Five steps of data science:
- Asking an interesting question
- Obtaining the data
- Exploring the data
- Modeling the data
- Communicating and visualizing the results
Relational Databases
- Database: an organized collection of data used to represent the set of information useful for the information system
- DBMS (Database Management System): a software that provides users and other applications access to a database
- Relational model: a data model that represents data as a 2-dimensional table (relation) with rows (tuples) and columns (fields)
- Primary key: a field that uniquely identifies each record in a table
- Foreign key: a field that refers to the primary key of another table
- DBMS properties:
- Data integrity: ensures data correctness
- Access authorization: controls access to the database
- Concurrent access control: handles multiple users accessing the same data
Database Integrity Constraints
- Entity integrity constraints: ensure primary key uniqueness and non-null values
- Domain integrity constraints: set rules for specific fields, e.g., value ranges, not null constraints
- Tuple integrity constraints: verify constraints on the tuple level
- Referential integrity constraints: ensure foreign key values exist in the referenced primary key
Recommender Systems
- Applications of data science
- Examples: music recommendation systems, shopping recommendation systems
- Trade-off: more information shared, better recommendations, but increased privacy concerns
- Ethical concerns: filter bubble, limiting exposure to new information
Data Science
- Involves skills in:
- Computer programming
- Statistics and math
- Domain knowledge
- The five steps of data science are used to extract knowledge from data and make predictions### Database Access and Authorization
- Access to a DBMS requires a credential (username and password) to authenticate and authorize access to the database.
Relational Model
- The Relational Model is used to represent information as data in a database.
- A relation is a table, and a database is composed of several tables.
- Tables have primary keys (unique identifier for each record) and foreign keys (links to related records in other tables).
Database Integrity
- Database integrity ensures that data is consistent and accurate.
- There are three types of integrity constraints:
- Entity Integrity: ensures that primary keys are unique and not null.
- Domain Integrity: ensures that data values are within a specific domain or range.
- Referential Integrity: ensures that relationships between tables are valid.
- Tuple Integrity: ensures that each tuple (record) is valid.
Referential Integrity
- Referential Integrity constraints ensure that relationships between tables are valid.
- An example of a referential integrity constraint is between the
examstable and thecoursestable, where thecoursefield inexamsmust match a value in thecode_coursefield incourses. - Referential integrity constraints can be one-way or two-way.
Operations that Violate Referential Integrity
- Four types of operations can violate referential integrity:
- Create: inserting a new record with a value that does not exist in the related table.
- Update: modifying a record in a way that breaks the relationship with another table.
- Delete: deleting a record that has related records in another table.
- DBMS can handle these operations in three ways:
- Restrict: prevents the operation from occurring.
- Cascade: automatically updates related records in other tables.
- Set NULL: sets the value of the related field to NULL.
Database Design
- Database design involves three steps:
- Conceptual Design: uses the Entity-Relationship (ER) model to represent the database structure.
- Logical Design: translates the ER model into a logical schema.
- Physical Design: implements the logical schema in a real database.
Indexes
- Indexes are data structures that improve the speed of SELECT operations.
- Indexes can be created on fields that are frequently used in WHERE clauses.
- Indexes can slow down INSERT, UPDATE, and DELETE operations.
ER Model
- The ER model is a conceptual representation of the database structure.
- The ER model consists of:
- Entities: classes of objects with common properties.
- Relationships: links between entities.
- Attributes: elementary properties that describe entities and relationships.
- Cardinality: minimum and maximum number of relationship occurrences.
- Keys: unique identifiers for entities.
Translating ER Model to Logical Schema
- The ER model is translated into a logical schema using rules and constraints.
- The logical schema is a representation of the database structure that is independent of the physical database.
Physical Design
- The physical design involves implementing the logical schema in a real database.
- The physical design requires choosing data types for each field.
- The choice of data type can impact the integrity and performance of the database.### Database Design
- In a database, an index is a data structure that improves the speed of data retrieval by allowing the database to quickly locate and retrieve specific data
- Indexes are created on fields in a table to speed up queries, but they require additional storage space and need to be updated when data is inserted, updated, or deleted
- Indexes are defined on one or more attributes (fields)
Conceptual Schema
- In a database design, the first step is to identify the entities (classes of objects having common properties)
- Entities and their corresponding attributes:
- Content: title, director, genre, category, languages, ID
- Actors: country, ID, name, genre, birthday
- Relationships between entities:
- Many-to-many relationships: Actors-Content (e.g., an actor can play in many movies, and a movie can have many actors)
Logical Schema
- Entities become tables or relations in the database
- Translation of entities to tables:
- Content (ID_content, director_content, genre_content, category_content, languages_content)
- Episodes (ID_episode, title_episode, season_episode, duration_episode, number_episode, ID_content(FK))
- Translation of relationships to tables:
- Play (ID_actor (FK), ID_content (FK), role_play)
- Feedback (ID_users (FK), ID_content (FK), type_feedback, datetime_feedback)
Recommender Systems
- A recommender system is a personalized information agent that provides recommendations based on some criteria
- Examples of recommender systems: Amazon, Netflix, Last.fm, Pandora
- How recommender systems work:
- Content-based filtering: recommends items with similar characteristics to the ones a user likes
- Collaborative filtering: recommends items based on the preferences of other users with similar tastes
Collaborative Filtering
- User-Item Matrix: a matrix where each row represents a user, each column represents an item, and each cell contains a rating or vote
- Steps to compute the predicted rating:
- Produce the users' distance matrix
- Normalize the distances
- Compute the similarity between users
- Select the neighbors (users with similar taste)
- Compute the predicted rating based on the neighbors' ratings
Advantages and Disadvantages of Collaborative Filtering
- Advantages:
- Easier to implement than content-based filtering
- Disadvantages:
- Cold-start problem: difficult to suggest items to new users or new items
- Data sparsity: difficulty in computing neighbors and predicting ratings
- Rich-get-richer effect: popular items become even more popular, while less popular items are neglected
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Description
This quiz covers the basics of data science, including chapter 1 material. Topics may include data analysis, machine learning, and visualization techniques.
