Syllabus Application
Information Systems
IE 413
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Information Systems - IE 413
Classroom:
FASS-G022
Level of course:
Undergraduate
Course Credits:
SU Credit:3.000, ECTS:5, Engineering:5
Prerequisites:
-
Corequisites:
Course Type:
Lecture
Instructor(s) Information
Ahmet Onur Durahim
- Email: onurdurahim@sabanciuniv.edu
onur.durahim@principai.com
Course Information
Catalog Course Description
Introduction to information systems implemented and utilized in today’s enterprises both as an operational and a ecision support systems; hardware and software used in information systems; database management systems and data modeling techniques; query languages, data warehousing concepts and architectures; business intelligence, data mining techniques, use of data warehousing and business intelligence in data-driven decision making; current trends in information technology such as cloud computing and big data.
Course Learning Outcomes:
| 1. | Analyze how information systems and technology contribute to achieving corporate objectives and competitive advantages. |
|---|---|
| 2. | Explain transactional and analytical processing in business applications and decision-making processes. |
| 3. | Describe the fundamentals of data warehousing, big data, and business intelligence, relevant concepts in business scenarios. |
| 4. | Explain analytical database models and apply data retrieval and analysis techniques using database and data analysis tools. |
Course Objective
• Understand the key components and functions of information systems in organizations.
• Apply database management concepts, data modeling, and SQL for data handling.
• Explain and use data warehousing, business intelligence, and data mining techniques for decision making.
• Evaluate current IT trends such as cloud computing, big data, and AI-driven solutions.
• Gain hands-on experience with large language models, practicing prompt engineering and leveraging LLM-based tools for coding, problem-solving, and business applications.
• Integrate traditional information system concepts with modern AI tools to support data-driven organizational strategies.
• Apply database management concepts, data modeling, and SQL for data handling.
• Explain and use data warehousing, business intelligence, and data mining techniques for decision making.
• Evaluate current IT trends such as cloud computing, big data, and AI-driven solutions.
• Gain hands-on experience with large language models, practicing prompt engineering and leveraging LLM-based tools for coding, problem-solving, and business applications.
• Integrate traditional information system concepts with modern AI tools to support data-driven organizational strategies.
Sustainable Development Goals (SDGs) Related to This Course:
| Industry, Innovation and Infrastructure |
Course Materials
Resources:
• R. Sharda, D. Delen, E. Turban, “Business Intelligence, Analytics, and Data Science: A Managerial Perspective”, 5th Edition, 2023
• Laudon K. C. and Laudon J. P., “Management Information Systems: Managing the Digital Firm”, 18th Edition, 2025
• Jale VanderPlas, “A Whirlwind Tour of Python”, (Free Online Version)
• Jale VanderPlas, “Python Data Science Handbook”, (Free Online Version)
• Raghu Ramakrishnan and Johannes Gehkre, “Database Management Systems”, 3rd Ed., Pearson
• Laudon K. C. and Laudon J. P., “Management Information Systems: Managing the Digital Firm”, 18th Edition, 2025
• Jale VanderPlas, “A Whirlwind Tour of Python”, (Free Online Version)
• Jale VanderPlas, “Python Data Science Handbook”, (Free Online Version)
• Raghu Ramakrishnan and Johannes Gehkre, “Database Management Systems”, 3rd Ed., Pearson
Technology Requirements:
Python programming, Database Management Systems, LLMs