COL362/7362/632 Introduction to Data Management

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🕰 Mon & Wed 11:00 -- 12:00 & Thu 12:00 -- 13:00 (Slot H)
📍 LH 108

Notice

Course Logistics

• Piazza for all course related discussion.
• Moodle (new) for lecture notes and assignments.
• Acadly for in-class participation.

Teaching Assistants

• Aishik Chanda
• Nilanjan Ghosh
• Mayank Shukla
• Priyadarshini Radhakrishnan
• Rahul
• Satyam Jay

Overview

From web applications and cloud services to artificial intelligence and large-scale analytics, databases silently enable systems to store, organize, and reason over massive volumes of data with reliability and efficiency. Rather than being a narrow subfield, database systems form a vertical slice across multiple areas of computer science, intersecting systems, algorithms, theory, storage, networking, security, and increasingly, machine learning.

Data management systems play a foundational role in data analytics. They provide structured and principled mechanisms for storing, querying, and retrieving data, enabling analysts and data scientists to efficiently extract meaningful information and insights from raw datasets. Without robust data management, analytics pipelines become brittle, inefficient, and unscalable.

The course “Introduction to Database Management Systems” provides students with a comprehensive understanding of the fundamental concepts and principles underlying the design and implementation of data management systems, with a primary focus on relational database management systems.

By the end of the course, students will develop a strong conceptual and practical foundation in data management systems, equipping them to reason about data-centric problems and preparing them for advanced coursework and professional roles in data analytics, software systems, and related fields.

Objectives

• Fundamentals: Develop a foundational understanding of the basic concepts and principles associated with data management systems.

• Relational Data Modeling: Introduce students to ER and relational data model and equip them with the skills to design and create effective database structures.

• Query Language Proficiency: Familiarize students with Relational Algebra and Structured Query Language (SQL) to enable them to interact with databases and perform analytical tasks including retrieval, data manipulation, and reporting.

• Database Design and Implementation: Provide knowledge and skills for designing and implementing databases systems, and applying them to broader data analytical tasks.

• Practical Application: Offer hands-on experience and practical exercises to apply theoretical knowledge, allowing students to develop the skills needed for real-world scenarios in data analytics.

Prerequisites

• COL 106
• Programming in Java/C/C++

Recommended Textbooks

B1. Database System Concepts (7 ed.) by Silberschatz, Korth and Sudarshan, McGraw-Hill.

B2. Database Systems: The Complete Book (2 ed.) by Garcia-Molina, Ullman, Widom, Prentice Hall.

Evaluation

Grading

Audit Criteria

• Overall 60% or more
• Score 60% or more in mid-term + end-term
• Attendance over 75%

Schedule