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Home/ GATE DA Syllabus/GATE Data Science and AI Syllabus | Database Management and Warehousing

GATE Data Science and AI Latest Articles

On: April 13, 2024 Posted in GATE DA Syllabus, Database Management and Warehousing Comments: 0 Views: 14

GATE Data Science and AI Syllabus | Database Management and Warehousing

GATE Data Science and AI DBMS and Warehousing Syllabus

ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations.

Best GATE Data Science & AI 2025 Course and Test Series

Here’s a breakdown of each topic:

ER-Model (Entity-Relationship Model):

The ER model is a conceptual data model used to describe the data requirements and constraints in a system. It represents entities as rectangles, attributes as ovals, and relationships as diamonds.

Relational Model:

The relational model is a mathematical model for data organization. It represents data in the form of tables (relations), where each row represents a record and each column represents an attribute.

Relational Algebra:

Relational algebra is a procedural query language used to manipulate data stored in relational databases. It consists of a set of operations such as selection, projection, join, union, intersection, and difference.

Tuple Calculus:

Tuple calculus is a non-procedural query language used to retrieve data from relational databases based on mathematical logic and set theory.

SQL (Structured Query Language):

SQL is a standard language for managing and manipulating relational databases. It allows users to perform various operations such as querying data, inserting, updating, and deleting records, and creating and modifying database schema.

Integrity Constraints:

Integrity constraints are rules that enforce data integrity and consistency in a database. They include entity integrity, referential integrity, domain integrity, and user-defined integrity constraints.

Normal Form:

Normal forms are guidelines for organizing relational database tables to minimize redundancy and dependency. Common normal forms include First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and Boyce-Codd Normal Form (BCNF).

File Organization:

File organization refers to the way data is stored and accessed in computer files. Common file organizations include sequential, indexed sequential, and direct (random) access.

Indexing:

Indexing is a technique used to improve the performance of database queries by creating indexes (data structures) on columns that are frequently used in search conditions.

Data Types:

Data types define the type of data that can be stored in a database column, such as integer, float, string, date, etc.

Data Transformation:

Data transformation involves converting data from one format to another to meet specific requirements. Common transformations include normalization (organizing data into tables), discretization (grouping continuous data into intervals), sampling (selecting a subset of data), and compression (reducing the size of data).

Data Warehouse Modeling:

Data warehouse modeling involves designing the schema and structure of a data warehouse to support multidimensional data analysis. It includes concepts such as star schema, snowflake schema, fact tables, dimension tables, concept hierarchies, and measures.

Understanding these concepts is essential for designing, querying, and managing relational databases and data warehouses effectively. They are fundamental to database management systems and play a crucial role in various applications across industries.

GATE DA Subject wise syllabus:

  1. GATE DA Linear Algebra Syllabus
  2. GATE DA Calculus and Optimization Syllabus
  3. GATE DA Probability and Statistics Syllabus
  4. GATE DA Python Programming Data Structures and Algorithms Syllabus
  5. GATE DA DBMS and Warehousing Syllabus
  6. GATE DA Machine Learning
  7. GATE DA  Artificial Intelligence Syllabus

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