Syllabus Of DM-
- Introduction to Data Management:
- Understanding data and its importance
- Historical perspective on data management
- Data management life cycle
- Data Modeling:
- Entity-Relationship (ER) modeling
- Relational data model
- Normalization techniques
- Data modeling tools (e.g., ERD diagrams)
- Database Systems:
- Introduction to database management systems (DBMS)
- Relational database management systems (RDBMS)
- NoSQL databases
- NewSQL databases
- In-memory databases
- SQL (Structured Query Language):
- SQL fundamentals
- SQL for data retrieval (SELECT statements)
- SQL for data manipulation (INSERT, UPDATE, DELETE)
- Joins and subqueries
- Data Storage and Indexing:
- Physical storage structures (e.g., B-trees)
- Indexing techniques
- Data compression
- Storage management and allocation
- Query Optimization and Performance Tuning:
- Query execution plans
- Index selection and optimization
- Performance tuning strategies
- Data Warehousing:
- Data warehouse architecture
- ETL (Extract, Transform, Load) processes
- Data modeling for data warehousing
- Data warehousing tools (e.g., ETL tools)
- Big Data and NoSQL Databases:
- Introduction to big data concepts
- NoSQL database types (e.g., document, key-value, column-family, graph)
- Handling unstructured and semi-structured data
- Data Governance and Security:
- Data governance principles
- Data quality management
- Data security and privacy
- Compliance and regulations (e.g., GDPR, HIPAA)
- Data Analytics and Business Intelligence:
- Data analytics techniques
- Data visualization
- Business intelligence tools
- Reporting and dashboards
- Data Management Tools and Technologies:
- Overview of data management tools (e.g., DBMS, ETL, BI)
- Cloud-based data solutions
- Data integration platforms
- Emerging Trends in Data Management:
- Data lakes and data hubs
- Data streaming and real-time analytics
- Machine learning and AI in data management