dbm502 | Graduate
This graduate-level course is 6 weeks To enroll, speak with an Enrollment Representative.
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- Explain the meaning of key data management questions and the importance of the answers.
- Explain the role of data in information systems and business.
- Distinguish between the three types of data management in modern organizations.
Data Architecture for Transactional Databases
- Explain the environment, roles, and skills needed to support transactional data management, including relational database environment, database designers, database developers, and database administrators.
- Explain and apply techniques for deciding what data you need for transactional data management, including entity relationship modeling and conceptual design.
Interacting with a Transactional Database
- Demonstrate how to use Data Manipulation Language (DML) to enter information into relational databases.
- Explain how to use Structured Query Language (SQL) to extract rows and columns of data from tables and combine data from multiple tables using SELECT.
- Distinguish between different approaches for managing data in relational databases, and explain when each might be used, including Applications with embedded SQL, Stored Procedures, and Triggers.
Data and Database Administration for Transactional Databases
- Explain why database and information security is important to an organization.
- Explain how database administrators and developers can protect the integrity and confidentiality of data against internal and external attacks.
- Explain how database administrators can protect data integrity against system and hardware outages.
Data Warehouses and Historical Data
- Explain the roles, skills, and tools needed for managing historical data via data warehouses.
- Explain how to select, clean, and bring data of interest into a data warehouse.
- Demonstrate techniques for designing an effective data warehouse, through star database structures, selecting and organizing fact and dimension tables, and specialized indexing (e.g., bitmap indexes).
Big Data and Management of Analytical Data
- Explain the skills and environments required for analytic data.
- Describe how those tools are used.
- Explain the business value of analytic results, such as data visualization and finding and applying patterns.
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