Industrial Engineering (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

COE103

Course Name:

Computational Thinking

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Compulsory Courses

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. MUHAMMED DAVUD

Course Lecturer(s):

Asst. Prof. Muhammed Davud

Course Assistants:

Course Objective and Content

Course Objectives:

This course offers a comprehensive exploration of computer science fundamentals. It covers programming languages, algorithm design, efficiency analysis, and various algorithm paradigms. Students will also apply computational thinking using Python.

Course Content:

This course aims to understand the fundamentals of computer science, including the significance of programming languages and the historical context of computing. It will focus on the design and analysis of algorithms with an emphasis on efficient problem-solving strategies. Students will evaluate the efficiency of algorithms considering time and space complexity and apply this knowledge to real-world problem-solving. Various algorithmic paradigms, such as Brute Force, Decrease-and-Conquer, Divide-and-Conquer, and Transform-and-Conquer, will be applied to address diverse problem domains. Additionally, students will apply computational thinking in practical contexts using Python, enhancing their ability to solve real-world problems effectively.

Learning Outcomes

The students who have succeeded in this course;
1) Understand the fundamentals of computer science, including the significance of programming languages and the historical context of computing.
2) Design and analysis algorithms, with a focus on efficient problem-solving strategies.
3) Evaluate the efficiency of algorithms, considering time and space complexity, and apply this knowledge to real-world problem-solving.
4) Apply different algorithmic paradigms, including Brute Force, Decrease-and-Conquer, Divide-and-Conquer, and Transform-and-Conquer, to address diverse problem domains.
5) Apply computational thinking in practical contexts using Python.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction
2)	Computer and Programming Languages
3)	Algorithm Design and Flow Charts
4)	Algorithm Design and Flow Charts
5)	Analysis of Algorithm Efficiency
6)	Brute Force and Exhaustive Search
7)	Decrease-and-Conquer
8)	Midterm
9)	Divide-and-Conquer
10)	Transform-and-Conquer
11)	Space and Time Tradeoffs
12)	Iterative Improvement Algorithms
13)	Introduction to Python
14)	Applied Computational Thinking Using Python

Sources

Course Notes / Textbooks:	1- Introduction to the Design & Analysis of Algorithms - 3rd edition, by Anany Levitin. 2- Computational Thinking, A beginner’s guide to problem-solving and programming, Karl Beecher, 2017.
References:	Lecture Notes.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2) Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.		2	2	2
3) Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.	2	2	2
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively.			3	3	3
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.	2
3)	Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.	2
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively.	3
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.
6)	Ability to work effectively within and multidisciplinary teams; individual study skills.
7)	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	13	39
Application	14	14
Study Hours Out of Class	14	28
Midterms	2	17
Final	1	15
Total Workload		113