Faculty of Computing and Informatics Science

Data Structures Laboratory

Faculty: Faculty of Computer and Information Sciences 

Location: 3rd Floor D3-01

Capacity: 60 Students

Responsible Faculty Member:

• Assoc. Prof. Dr. Kürşat Mustafa KARAOĞLAN

Laboratory Supervisor:

• Research Assistant Saliha ÖZGÜNGÖR

Laboratory View

Purpose of the Laboratory

The Data Structures Laboratory aims for students to learn data organization, algorithm analysis and memory management topics in a practical manner. This laboratory experimentally teaches how basic data structures (stack, queue, linked list, tree, graph, etc.) are designed, implemented and analyzed in programming languages (especially C/C++ and Python). The aim is to equip students with algorithmic thinking, efficient memory usage and problem-solving skills.

Laboratory Usage Rules

• The laboratory should only be entered during class or project hours, under the supervision of an instructor or authorized personnel.
• Computers, network devices (switch, router, etc.) and cabling systems should only be used for experimental purposes. Connections should not be energized before the experiment without being shown to the instructor.
• Approval must be obtained from the instructor before any connection or configuration process.
• Unauthorized intervention in laboratory software, uploading personal files or changing internet settings is prohibited.
• Each student must keep experiment reports regularly and submit them at the end of the course.
• At the end of the experiment, connections must be disconnected, computers must be shut down and the desk arrangement must be maintained.
• In case of damage to laboratory equipment or detection of failure, the situation must be reported to the responsible personnel immediately.

Areas of Use

• Data Structures and Algorithms course applications
• Data structure development in C, C++ or Python programming environments
• Stack, Queue and Linked List applications
• Implementation of Tree and Graph data structures
• Analysis of Sorting and Searching algorithms
• Time complexity (Big-O) calculations and performance measurements
• Graduation projects and algorithm-based academic research

Learning Outcomes

• Explains and implements the logic of basic data structures (stack, queue, linked list, tree, graph).
• Analyzes the performance of different algorithms and selects the appropriate data structure.
• Performs dynamic memory management (malloc/free, new/delete) operations through programming language.
• Implements sorting, searching and traversal algorithms.
• Measures time and memory efficiency, evaluates algorithm complexity.
• Produces solutions to complex problems with algorithmic thinking.
• Develops teamwork, code versioning (e.g. Git) and technical reporting skills.
• Acquires coding habits in accordance with engineering ethics and software standards.