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First Normal Form (1NF) requires that a relational database table has columns with atomic values and each entry in a column holds a single value, ensuring no repeating groups or arrays. This normalization step is crucial for eliminating data redundancy and improving data integrity in database systems.
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Second Normal Form (2NF) helps make sure that a table in a database is organized so that all the information in each row depends only on the whole key, not just part of it. This makes the database easier to understand and helps prevent mistakes or confusion when adding or changing data.
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Third Normal Form (3NF) is a database normalization stage that ensures all the attributes in a table are functionally dependent only on the primary key, eliminating transitive dependency. Achieving 3NF optimizes database design by reducing redundancy and improving data integrity.
Boyce-Codd Normal Form (BCNF) is a higher version of database normalization that aims to eliminate redundancy and dependency anomalies by ensuring that every determinant is a candidate key. It is stricter than the Third Normal Form (3NF) and is used to ensure the integrity and efficiency of database designs.
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Functional dependency is a constraint between two sets of attributes in a relational database, where one set uniquely determines another. It is fundamental to database normalization, ensuring data integrity and reducing redundancy by organizing data efficiently.
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Data redundancy occurs when the same piece of data is stored in multiple places within a database or data storage system, which can lead to inconsistencies and increased storage costs. While sometimes intentional for backup and performance reasons, excessive redundancy can complicate data management and compromise data integrity.
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Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle, ensuring that it remains unaltered and trustworthy for decision-making and analysis. It is crucial for maintaining the credibility of databases and information systems, and involves various practices and technologies to prevent unauthorized access or corruption.
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A database schema is a structured framework that defines the organization of data within a database, including tables, fields, relationships, views, and indexes. It serves as a blueprint for how data is stored, accessed, and managed, ensuring consistency and integrity across the database system.
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A relational database is a structured collection of data that uses a schema to define relationships between tables, enabling efficient data retrieval and manipulation through SQL queries. It ensures data integrity and reduces redundancy by organizing data into tables where each row is a unique record identified by a primary key.
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Feature engineering is the process of transforming raw data into meaningful inputs for machine learning models, enhancing their predictive power and performance. It involves creating new features, selecting relevant ones, and encoding them appropriately to maximize the model's ability to learn patterns from data.
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A database management system (DBMS) is software that facilitates the creation, manipulation, and administration of databases, enabling users to store, retrieve, and manage data efficiently. It ensures data integrity, security, and consistency while supporting concurrent access and complex queries, making it essential for modern data-driven applications.
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SQL, or Structured Query Language, is a standardized programming language used for managing and manipulating relational databases. It allows users to perform tasks such as querying data, updating records, and managing database structures with ease and efficiency.
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A primary key is a unique identifier for a record in a database table, ensuring that each entry is distinct and easily retrievable. It is essential for maintaining data integrity and establishing relationships between tables in a relational database management system.
A relational database management system (RDBMS) is software that manages data stored in a structured format, using tables with rows and columns to ensure data integrity and efficient querying. RDBMSs use SQL for database access and manipulation, supporting complex transactions and ensuring ACID compliance for reliable data operations.
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The Entity-Relationship Model is a high-level conceptual framework used to define the data structure of a database in terms of entities, attributes, and relationships. It serves as a blueprint for designing and organizing data, ensuring clarity and coherence in database management systems.
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An entity set is a collection of similar entities in a database, where each entity represents a real-world object or concept with a unique identity. It serves as a fundamental component in database design, particularly in the Entity-Relationship model, facilitating the organization and retrieval of data by grouping related entities together.
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Database design is the process of structuring a database in a way that ensures data consistency, integrity, and efficiency in storage and retrieval. It involves defining tables, relationships, and constraints to optimize performance and meet the specific needs of applications and users.
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Batch Normalization is a technique to improve the training of deep neural networks by normalizing the inputs to each layer, which helps in reducing internal covariate shift and accelerates convergence. It allows for higher learning rates, reduces sensitivity to initialization, and can act as a form of regularization to reduce overfitting.
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Preprocessing techniques are crucial steps in data preparation that enhance the quality of data for analysis and machine learning models, ensuring more accurate and efficient results. These techniques involve cleaning, transforming, and organizing raw data into a structured format suitable for analysis, addressing issues like missing values, noise, and inconsistencies.
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A composite key is a combination of two or more columns in a database table that together uniquely identify a row, ensuring data integrity when no single column can serve this purpose alone. It is essential in relational databases where complex relationships between tables require unique identification through multiple attributes.
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Schema design is the process of defining the structure of a database, ensuring efficient data storage, retrieval, and integrity. It involves organizing data elements and their relationships to support application requirements and scalability while minimizing redundancy and potential anomalies.
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Database optimization involves improving the performance and efficiency of a database system, ensuring it can handle large volumes of data and queries quickly. It encompasses various techniques and strategies to enhance query speed, storage efficiency, and overall system responsiveness.
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A zero-centered distribution is a probability distribution where the mean is zero, often used in statistical models to simplify calculations and ensure symmetry around the origin. This characteristic is particularly useful in machine learning and finance, where it helps in normalizing data and reducing bias in predictive models.
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An Equi Join is a type of join operation in relational databases that combines rows from two or more tables based on a common column with equal values. It is the most common form of join, often used to retrieve related data from multiple tables by matching columns with the same data type and value.
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Life Cycle Impact Assessment (LCIA) is a systematic process used to evaluate the environmental impacts associated with the stages of a product's life cycle, from raw material extraction to disposal. It helps in identifying and quantifying the potential environmental effects of a product or process, enabling more sustainable decision-making in product design and policy development.
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The wave function is a fundamental concept in quantum mechanics that describes the quantum state of a system, encoding information about the probability amplitudes of a particle's position, momentum, and other physical properties. It is typically represented as a complex-valued function, and its squared magnitude gives the probability density of finding a particle in a particular state or location.
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Relative frequency is the ratio of the number of times an event occurs to the total number of trials or observations, providing an empirical probability estimate. It is a fundamental concept in statistics and probability, used to understand the likelihood of events based on observed data.
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Preprocessing is a crucial step in data analysis and machine learning that involves transforming raw data into a clean and usable format, enhancing the quality and performance of the models. It encompasses a range of techniques such as data cleaning, normalization, and feature extraction to ensure that the data is consistent, complete, and ready for analysis.
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Data standardization is the process of transforming data into a common format to enable consistent analysis and comparison across different datasets. It is crucial for ensuring data integrity and interoperability, especially in environments where data is aggregated from multiple sources.
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Address standardization is the process of converting addresses into a consistent format to improve accuracy and efficiency in data handling, mailing, and location-based services. It involves correcting errors, abbreviating terms, and ensuring compliance with postal standards to facilitate seamless integration across various systems.
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