STAT311 MODERN DATABASE SYSTEMS
| Course Code: | 2460311 |
| METU Credit (Theoretical-Laboratory hours/week): | 4 (3.00 - 2.00) |
| ECTS Credit: | 6.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. CEYLAN YOZGATLIGİL |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
This course introduces the fundamental concepts of database management systems. It covers relational database theory, database design, normalization, query processing with SQL, and basic transaction management. Students will gain both theoretical understanding and practical skills by designing and implementing databases.
Course Content
Introduction to database systems. Relational databases. Entity relationship (ER) model. Normalization. Structured Query Language (SQL). Designing databases. Introduction to distributed, parallel and object databases. Big data storage systems. Datawarehouses. Online Analytic Processing (OLAP). Big data analytics and NoSQL. Web data management. Cloud computing.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Understand the concepts of databases and database management systems.
- Model data using the Entity-Relationship (ER) model and convert ER diagrams into relational schemas.
- Apply relational algebra to express queries formally.
- Use Structured Query Language (SQL) to create, query, and manipulate relational databases.
- Apply functional dependency theory and normalization techniques to improve database design.
- Understand basic concepts of transaction management, concurrency control, and recovery.
- Implement a database project with presentation, logic, and data layers, including CRUD operations.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution