ADVANCED DATABASE MANAGEMENT SYSTEMS

Course Description

The course covers a variety of advanced topics in database management systems and contemporary database applications using a problem-based learning method. Advanced concurrency control techniques, relational database query processing and optimization strategies, advanced indexing techniques, parallel and distributed database systems, next-generation data models, data mining on large databases, data on the web, and topics in data security and privacy are just a few of the specific topics covered. Upon completion of the course, students will have a broad understanding of relational systems, including data models, database designs, and database manipulations. Students will also learn about the newest and most recent advancements and trends in database management systems, such as the internet database environment and data warehousing.

ADVANCED DATABASE MANAGEMENT SYSTEMS

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

1. 01 TO DEVELOP SKILLS FOR DESIGNING AND IMPLEMENTING A RELATIONAL DATABASE
2. 02 TO GIVE STUDENTS AN UNDERSTANDING OF THE FUNDAMENTAL CONCEPTS IN RELATIONAL THEOREY
3. 03 TO GIVE STUDENTS A WORKING KNOWLEDGE OF IMPLEMENTATION OF DATABASE DESIGN
4. 04 TO GIVE STUDENTS BACKGROUND ON STORED PROCEDURES, FUNCTIONS AND TRIGGERS
5. 05 TO ENABLE STUDENTS IN THE USE OF SQL FOR DML AND DDL
6. 06 TO INTRODUCE THE STUDENTS HOW TO DEVLOP DESKTOP APPLICATIONS WITH RDMS

Course Syllabus

Reference Books & Course Materials

1. 01 "FUNDAMENTALS OF DATABASE SYSTEMS", ELMASRI AND NAVTHE, , 5th edition, 2007, ADDISON WESLEY

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

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