Details of CS2203 (Spring 2027)
| Level: 2 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| CS2203 | Database Management Systems | Saptarshi Pyne |
| Preamble |
|---|
| Data is the new oil. From mobile banking applications to online shops to social media platforms, the data behind every application is efficiently managed by a particular type of software known as the database management system (DBMS).
Modern applications, such as social media platforms, require many additional elements compared to traditional applications, such as internet banking portals. In this course, we will learn how to design DBMS software for both modern and traditional applications. |
| Syllabus |
|---|
| 1. Database System Concepts and Architecture: Data Models, Schemas, and Instances; Three-Schema Architecture and Data Independence; Database Languages and Interfaces; Centralized and Client/Server Architectures for DBMS.
2. Data Modeling: Entity-Relationship Diagram; Relational Model - Constraints, Languages, Design, and Programming; Relational Database Schemas; Update Operations and Dealing with Constraint Violations; Relational Algebra and Relational Calculus; Codds Rules. 3. SQL: Data Definition and Data Types; Constraints, Queries, Insert, Delete, and Update Statements; Views, Stored Procedures and Functions; Database Triggers, SQL Injection. 4. Normalization for Relational Databases: Functional Dependencies and Normalization; Algorithms for Query Processing and Optimization; Transaction Processing; Concurrency Control Techniques; Database Recovery Techniques; Object and Object-Relational Databases; Database Security and Authorization. 5. Enhanced Data Models: Temporal Database Concepts; Multimedia Databases; Deductive Databases; XML and Internet Databases; Mobile Databases; Geographic Information Systems; Genome Data Management; Distributed Databases and Client- Server Architectures. 6. Data Warehousing and Data Mining: Data Modeling for Data Warehouses; Concept Hierarchy; OLAP and OLTP; Association Rules, Classification, Clustering, Regression, Support Vector Machine, K-Nearest Neighbors, Hidden Markov Model, Summarization, Dependency Modeling, Link Analysis, Sequencing Analysis, Social Network Analysis. 7. Big Data Systems: Big Data Characteristics; Types of Big Data; Big Data Architecture; Introduction to Map-Reduce and Hadoop; Distributed File System, HDFS. 8. NOSQL: NOSQL and Query Optimization; Different NOSQL Products; Querying and Managing NOSQL; Indexing and Ordering Data Sets; NOSQL in Cloud. |
| Prerequisite |
|---|
| CS1101: Introduction to Computer Programming (or the permission of instructor(s)) |
| References |
|---|
| References
Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems. 7th Edition. Pearson Education. Abraham Silberschatz, A., Korth H. F., Sudarshan, S. (2021). Database System Concepts. 7th Edition. McGraw Hill. Ramakrishnan, R., & Gehrke, J. (2002). Database Management Systems. 3rd Edition. McGraw Hill Education. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 2 | Elective |
| 2 | IP | 4 | Elective |
| 3 | MP | 2 | Not Allowed |
| 4 | MP | 4 | Not Allowed |
| 5 | MR | 2 | Not Allowed |
| 6 | MR | 4 | Not Allowed |
| 7 | MS | 10 | Elective |
| 8 | MS ( Computational and Data Sciences ) | 4 | Core |
| 9 | MS | 6 | Elective |
| 10 | MS | 8 | Elective |
| 11 | RS | 1 | Elective |
| 12 | RS | 2 | Elective |